{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ECG5000 Time Series Anomaly Detection - Exploratory Data Analysis\n",
    "\n",
    "## Project Overview\n",
    "\n",
    "This notebook provides a comprehensive exploratory data analysis (EDA) for the ECG5000 dataset, focusing on unsupervised anomaly detection.\n",
    "\n",
    "### Objectives:\n",
    "1. Understand the data structure and characteristics\n",
    "2. Analyze time series patterns and distributions\n",
    "3. Identify potential anomaly indicators\n",
    "4. Prepare data for unsupervised modeling\n",
    "5. Extract meaningful features for anomaly detection\n",
    "\n",
    "### Important Notes:\n",
    "- Unsupervised Approach: Labels are NOT used for training\n",
    "- Evaluation Only: Labels are only used for performance evaluation\n",
    "- Real-world Scenario: Simulates conditions where anomaly labels are unavailable\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Loading and Initial Exploration\n",
    "\n",
    "### Purpose:\n",
    "- Load the balanced ECG5000 dataset\n",
    "- Understand the data structure\n",
    "- Check data quality and basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported successfully\n",
      "Starting comprehensive EDA...\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import joblib\n",
    "from scipy import stats\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.stats import pearsonr\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Set plotting style for better visualizations\n",
    "plt.style.use('default')\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "plt.rcParams['font.size'] = 10\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"Libraries imported successfully\")\n",
    "print(\"Starting comprehensive EDA...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ECG5000 dataset...\n",
      "Data loaded successfully!\n",
      "Dataset shape: (2000, 141)\n",
      "Number of features: 140\n",
      "Anomaly rate: 11.00%\n",
      "Total samples: 2000\n"
     ]
    }
   ],
   "source": [
    "# Load the original ECG5000 dataset\n",
    "print(\"Loading ECG5000 dataset...\")\n",
    "df = pd.read_csv('../data/ECG5000_balanced.csv')\n",
    "\n",
    "# Extract components\n",
    "X = df.drop('target', axis=1)  # Time series features\n",
    "y = df['target']  # Labels (for evaluation only)\n",
    "feature_names = X.columns.tolist()\n",
    "\n",
    "# Calculate anomaly rate\n",
    "anomaly_rate = (y != 1).mean()  # Class 1 is normal, others are anomalies\n",
    "\n",
    "print(f\"Data loaded successfully!\")\n",
    "print(f\"Dataset shape: {df.shape}\")\n",
    "print(f\"Number of features: {len(feature_names)}\")\n",
    "print(f\"Anomaly rate: {anomaly_rate:.2%}\")\n",
    "print(f\"Total samples: {len(y)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET OVERVIEW:\n",
      "==================================================\n",
      "Shape: (2000, 141)\n",
      "Memory usage: 2.15 MB\n",
      "Data types: {dtype('float64'): 140, dtype('int64'): 1}\n",
      "\n",
      "FIRST 5 ROWS:\n",
      "       att1      att2      att3      att4      att5      att6      att7  \\\n",
      "0 -0.635587 -1.594733 -1.792081 -2.377737 -2.442025 -2.308995 -2.311829   \n",
      "1  1.203693  1.676928  0.428162 -1.521430 -2.894471 -4.303335 -4.376221   \n",
      "2 -1.707111 -3.080636 -3.681314 -4.397280 -4.542173 -3.242822 -0.993969   \n",
      "3  4.261646  2.268752 -0.135173 -1.615500 -2.635026 -2.517345 -1.344537   \n",
      "4 -1.314147 -3.122485 -4.068301 -4.275174 -3.884163 -3.148948 -1.595125   \n",
      "\n",
      "       att8      att9     att10  ...    att132    att133    att134    att135  \\\n",
      "0 -2.253411 -2.039034 -1.512639  ... -2.031118 -2.772653 -3.480162 -3.874910   \n",
      "1 -3.316617 -2.082244 -1.881593  ...  0.537608  1.071678  0.937726  1.701423   \n",
      "2 -0.312374 -0.337333 -0.286812  ...  0.623562  0.110638 -1.309685 -1.045666   \n",
      "3 -0.501953 -0.466358  0.309286  ... -1.752851 -1.004001 -0.740441 -0.310276   \n",
      "4 -0.956926 -0.737890 -0.092787  ...  1.402357  1.098818  0.751063 -0.218014   \n",
      "\n",
      "     att136    att137    att138    att139    att140  target  \n",
      "0 -3.913596 -3.157701 -2.091301 -1.547585  0.149625       2  \n",
      "1  2.170401  2.363171  1.913464  0.953470  1.550830       1  \n",
      "2 -0.659981 -0.902732 -1.070214 -0.641128 -1.905541       1  \n",
      "3 -0.226964  0.173336  0.222312 -0.330147  0.019939       1  \n",
      "4 -1.167342 -1.673711 -1.971539 -1.298575 -1.200599       1  \n",
      "\n",
      "[5 rows x 141 columns]\n"
     ]
    }
   ],
   "source": [
    "# Convert to DataFrame for easier analysis\n",
    "df = pd.DataFrame(X, columns=feature_names)\n",
    "df['target'] = y\n",
    "\n",
    "print(\"DATASET OVERVIEW:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Shape: {df.shape}\")\n",
    "print(f\"Memory usage: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
    "print(f\"Data types: {df.dtypes.value_counts().to_dict()}\")\n",
    "\n",
    "print(\"\\nFIRST 5 ROWS:\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Quality Assessment\n",
    "\n",
    "### Purpose:\n",
    "- Check for missing values\n",
    "- Identify duplicate records\n",
    "- Verify data types and ranges\n",
    "- Calculate basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MISSING VALUES ANALYSIS:\n",
      "========================================\n",
      "Empty DataFrame\n",
      "Columns: [Missing_Count, Missing_Percentage]\n",
      "Index: []\n",
      "No missing values found!\n",
      "\n",
      "DUPLICATE ANALYSIS:\n",
      "========================================\n",
      "Duplicate rows: 0\n",
      "No duplicate records found!\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(\"MISSING VALUES ANALYSIS:\")\n",
    "print(\"=\" * 40)\n",
    "missing_values = df.isnull().sum()\n",
    "missing_percentage = (missing_values / len(df)) * 100\n",
    "\n",
    "missing_df = pd.DataFrame({\n",
    "    'Missing_Count': missing_values,\n",
    "    'Missing_Percentage': missing_percentage\n",
    "})\n",
    "\n",
    "print(missing_df[missing_df['Missing_Count'] > 0])\n",
    "if missing_df['Missing_Count'].sum() == 0:\n",
    "    print(\"No missing values found!\")\n",
    "\n",
    "# Check for duplicates\n",
    "print(f\"\\nDUPLICATE ANALYSIS:\")\n",
    "print(\"=\" * 40)\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"Duplicate rows: {duplicates}\")\n",
    "if duplicates == 0:\n",
    "    print(\"No duplicate records found!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DESCRIPTIVE STATISTICS:\n",
      "========================================\n",
      "           att1      att2      att3      att4      att5      att6      att7  \\\n",
      "count  2000.000  2000.000  2000.000  2000.000  2000.000  2000.000  2000.000   \n",
      "mean     -0.364    -2.074    -3.090    -3.740    -3.675    -3.135    -2.262   \n",
      "std       1.266     1.481     1.172     0.969     0.800     0.799     0.699   \n",
      "min      -6.028    -6.281    -5.110    -5.325    -5.376    -5.217    -4.376   \n",
      "25%      -1.234    -3.162    -3.989    -4.379    -4.159    -3.688    -2.754   \n",
      "50%      -0.458    -2.280    -3.375    -4.082    -3.865    -3.194    -2.197   \n",
      "75%       0.459    -1.178    -2.420    -3.371    -3.438    -2.654    -1.793   \n",
      "max       4.966     3.351     1.628     1.250     0.957     0.987     1.436   \n",
      "\n",
      "           att8      att9     att10  ...    att132    att133    att134  \\\n",
      "count  2000.000  2000.000  2000.000  ...  2000.000  2000.000  2000.000   \n",
      "mean     -1.659    -1.232    -0.701  ...     0.720     0.761     0.690   \n",
      "std       0.511     0.549     0.562  ...     0.948     1.070     1.268   \n",
      "min      -3.652    -3.675    -3.273  ...    -3.525    -3.890    -4.064   \n",
      "25%      -1.882    -1.575    -1.056  ...     0.465     0.612     0.453   \n",
      "50%      -1.644    -1.303    -0.683  ...     0.996     1.065     1.079   \n",
      "75%      -1.444    -0.956    -0.348  ...     1.285     1.358     1.420   \n",
      "max       1.471     1.684     1.899  ...     3.008     2.723     2.832   \n",
      "\n",
      "         att135    att136    att137    att138    att139    att140    target  \n",
      "count  2000.000  2000.000  2000.000  2000.000  2000.000  2000.000  2000.000  \n",
      "mean      0.545     0.281     0.165     0.196     0.286    -0.578     1.137  \n",
      "std       1.430     1.501     1.504     1.402     1.386     1.306     0.450  \n",
      "min      -4.332    -4.911    -4.670    -5.205    -5.639    -5.798     1.000  \n",
      "25%       0.169    -0.280    -0.437    -0.449    -0.440    -1.457     1.000  \n",
      "50%       1.003     0.746     0.663     0.598     0.631    -0.544     1.000  \n",
      "75%       1.429     1.271     1.164     1.172     1.259     0.353     1.000  \n",
      "max       2.815     2.539     3.433     3.579     4.426     7.402     5.000  \n",
      "\n",
      "[8 rows x 141 columns]\n",
      "\n",
      "INFINITE VALUES:\n",
      "========================================\n",
      "Infinite values: 0\n",
      "No infinite values found!\n"
     ]
    }
   ],
   "source": [
    "# Basic statistics\n",
    "print(\"DESCRIPTIVE STATISTICS:\")\n",
    "print(\"=\" * 40)\n",
    "stats_df = df.describe()\n",
    "print(stats_df.round(3))\n",
    "\n",
    "# Check for infinite values\n",
    "print(f\"\\nINFINITE VALUES:\")\n",
    "print(\"=\" * 40)\n",
    "infinite_count = np.isinf(df.select_dtypes(include=[np.number])).sum().sum()\n",
    "print(f\"Infinite values: {infinite_count}\")\n",
    "if infinite_count == 0:\n",
    "    print(\"No infinite values found!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Target Variable Analysis\n",
    "\n",
    "### Purpose:\n",
    "- Analyze class distribution\n",
    "- Understand balance between normal and anomaly classes\n",
    "- Visualize the labeling scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TARGET VARIABLE ANALYSIS:\n",
      "==================================================\n",
      "Class Distribution:\n",
      "  Class 1 (Normal): 1780 samples (89.00%)\n",
      "  Class 2 (Anomaly): 192 samples (9.60%)\n",
      "  Class 3 (Anomaly): 6 samples (0.30%)\n",
      "  Class 4 (Anomaly): 18 samples (0.90%)\n",
      "  Class 5 (Anomaly): 4 samples (0.20%)\n",
      "\n",
      "SUMMARY:\n",
      "  Total samples: 2000\n",
      "  Normal samples: 1780\n",
      "  Anomaly samples: 220\n",
      "  Anomaly rate: 11.00%\n"
     ]
    }
   ],
   "source": [
    "# Analyze target variable distribution\n",
    "print(\"TARGET VARIABLE ANALYSIS:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Class distribution\n",
    "class_counts = df['target'].value_counts().sort_index()\n",
    "class_percentages = (class_counts / len(df)) * 100\n",
    "\n",
    "print(\"Class Distribution:\")\n",
    "for class_label, count in class_counts.items():\n",
    "    percentage = class_percentages[class_label]\n",
    "    status = \"Normal\" if class_label == 1 else \"Anomaly\"\n",
    "    print(f\"  Class {class_label} ({status}): {count} samples ({percentage:.2f}%)\")\n",
    "\n",
    "print(f\"\\nSUMMARY:\")\n",
    "print(f\"  Total samples: {len(df)}\")\n",
    "print(f\"  Normal samples: {class_counts[1]}\")\n",
    "print(f\"  Anomaly samples: {class_counts.sum() - class_counts[1]}\")\n",
    "print(f\"  Anomaly rate: {anomaly_rate:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5tmhgdhy4IMpcAAAAAABgszvmkCat/11LDm6U1TDMjgOUODnHTunkvX0V0udhlRnyrCye1HMoPH5aAAAAAACA1h7doUkbfteG6N1mRwFKPqtVCZ/8oPS12xX5zTB5VIgwOxFcBGUuAAAAAACl2KrDW/X56l+4qRlggsxNu3X8jl4K//QN+d/Zwuw4cAGUuQAAAAAAlEKbj+/VJ6uma+s/+8yOApRq1nOJinl0kEJe6qEyrz8ri7u72ZHgxChzAQAAAAAoRXbHHNInq37S2qM7zI4C4ALDUMLH05SxcZciJgyVR2Q5sxPBSVHmAgAAAABQChyKO6FPV/+kJQc2mh0FQD4y1m7XiTueVvgXb8mvdVOz48AJUeYCAAAAAFCCnU4+q49XTtefe1fJahhmxwFwBbln4nXqwZdV5vVnFdrvMbPjwMlQ5gIAAAAAUAJlZGdpysY5mrxhtjJyMs2OA8AeVqvOvfOVsvYcUthHg+Xm6212IjgJylwAAAAAAEqY+X+v1UcrpulkUpzZUQBcg5QZi5R1MFrlvxslj6hws+PACVDmAgAAAABQQvwde1TvL/lWm0/sNTsKgCKSe/qsZLGYHQNOgjIXAAAAAAAXdy4tSZ+umq6ZO5cyLy5Qglh8vBQ5daQ8yoeZHQVOgjIXAAAAAAAXNmPnEn24fJqSMlLNjgKgiIV9OEg+jeuYHQNOhDIXAAAAAAAXdCz+lIYv+Fqbju8xOwqAYhDS91EF3t/e7BhwMpS5AAAAAAC4kBxrrqZsmK0J62YoMyfb7DgAioFfx1tV5o3eZseAE6LMBQAAAADARew6dUjDFnyl/WeizY4CoJh41ammiC/elMXNzewocEKUuQAAAAAAOLm0rAx9tvon/bBlHjc4A0owt7LBivxutNwC/MyOAidFmQsAAAAAgBPbcfKAXv/zUx1POG12FADFydNDkZPeked15c1OAidGmQsAAAAAgBPKsebqqzW/aeL6Wco1rGbHAVDMwt4bKN9bGpodA06OMhcAAAAAACdzLP6UXv/jU+2KOWR2FAAOEPzs/Qp6vKvZMeACKHMBAAAAAHAiP29bqLHLvldGTqbZUQA4gG+bZio74kWzY8BFUOYCAAAAAOAEzqYmauj8r7Ti8BazowBwEM8a1ynim2GyuLubHQUugjIXAAAAAACTrT+2U4P/+FTn0hLNjgLAQdyCAxT5/Si5BweaHQUuhDIXAAAAAACTGIahr9fN1BdrfpHVMMyOA8BR3N0V8c1weVW/zuwkcDGUuQAAAAAAmCAxPUVv/PmZVh7ZanYUAA5WdviL8mvTzOwYcEGUuQAAAAAAONjumEN6ZfaHOpkUZ3YUAA4W9MTdCul9v9kx4KIocwEAAAAAcKCfty3QB0u/U1ZuttlRADiYzy0NVW70ALNjwIVR5gIAAAAA4ADp2ZkasfBr/bFnldlRAJjAo3J5RU4aIYsndRyuHj89AAAAAAAUs9PJZ9Vv1hjtPX3E7CgATGAJ8FPkd6PlXjbE7ChwcW5mHnzFihXq2rWroqKiZLFYNGvWrDzrn3zySVksljxfHTt2zDPm3LlzevTRRxUUFKSQkBD16tVLKSkpecbs2LFDrVq1ko+PjypVqqT333+/uF8aAAAAAACSpB2nDqjH929Q5AKllZubIr58S961qxU47ETCaQcFgisztcxNTU1VgwYN9Nlnn+U7pmPHjjp16pTt68cff8yz/tFHH9Xu3bu1cOFCzZ07VytWrFDv3r1t65OSktS+fXtVrlxZmzdv1gcffKChQ4dqwoQJxfa6AAAAAACQpD/2rNIzPw1XXGqC2VEAmKTMG73l36FlgWPm/b1G90waqGlb/nJQKrgqU6dZ6NSpkzp16lTgGG9vb0VGRl523d69ezVv3jxt3LhRTZs2lSR98skn6ty5s8aMGaOoqChNmzZNWVlZmjRpkry8vFS3bl1t27ZN48aNy1P6AgAAAABQlD5b9bMmrJthdgwAJgp4sINC+z5a4Jg9MYf19rwvlWPN1ftLvtWRs/9ocNun5OHm7qCUcCVOP2fusmXLFB4ertDQUN1xxx165513VLZsWUnS2rVrFRISYityJaldu3Zyc3PT+vXr1a1bN61du1a33XabvLy8bGM6dOig9957T/Hx8QoNDb3kmJmZmcrMzLQ9T0pKkiRZrVZZrdbieqlXZDHtyHB1ln/9F7CHmf/uASWZ1WqVYRj8HYNL4ucWKFhWTrbemveF/vp7jdlRAJjIu0kdhY97rcAxZ1Li1W/WGGXkZNmW/bJ9kaLjYzT2noEK9PYr7phwMU5d5nbs2FHdu3dX1apVdejQIb3++uvq1KmT1q5dK3d3d8XExCg8PDzPNh4eHipTpoxiYmIkSTExMapatWqeMREREbZ1lytzR40apWHDhl2y/MyZM8rIyCiql2e38h4hph0brs0iKdTdXxZJhtlh4HJiY2PNjgCUSFarVYmJiTIMQ25ups58BdgtOTnZ7AiA00pIT1a/WWO07Z99ZkcBYCL3qHBFfjtSFm+vfMdk5mSp/6wxik05d8m69dG71Gv6MH1232CFBVzaXaH0cuoy9+GHH7Y9rlevnurXr6/q1atr2bJlatu2bbEdd8iQIRo4cKDteVJSkipVqqSwsDAFBQUV23Gv5FROgmnHhmu7UOLG5CRQ5sJu/37TDEDRsFqtslgsCgsLo8yFy/Hx8TE7AuCUYpLi9MKvo3T43D9mRwFgIoufj8pPHSmPiLIFjhs6f4J2xRzKd/2+M8f0xA9v6Yv7h6hKmaiijgkX5dRl7r9Vq1ZN5cqV08GDB9W2bVtFRkZecsVYTk6Ozp07Z5tnNzIyUqdP570b4IXn+c3F6+3tLW9v70uWu7m5mfrLFiUcrpUhfo5gP0omoPhYLBbTzy+Aq8HPLHCpI2f/0fO/jlRM8lmzowAwk8Wi8I9fl3eDWgUOm7h+lv7cu+qKuzuZdEZP/vi2Pu0+WDeWr15UKeHCXOos7MSJEzp79qzKly8vSWrRooUSEhK0efNm25glS5bIarXqpptuso1ZsWKFsrOzbWMWLlyoWrVqXXaKBQAAAAAA7LHr1CE9NX0oRS4Ahb7cUwH33F7gmGUHN+mTlT8Vep/x6cl65ufhWn1k+7XGQwlgapmbkpKibdu2adu2bZKkI0eOaNu2bYqOjlZKSopeffVVrVu3TkePHtXixYt1zz33qEaNGurQoYMkqXbt2urYsaOeffZZbdiwQatXr9aLL76ohx9+WFFR5y8/79Gjh7y8vNSrVy/t3r1bP/30k8aPH59nGgUAAAAAAK7GumM79ezPIxSfzlzSQGnn37WNQl97usAxB85Ea8gfn8qw83Oz6dmZ6jvzfc3ds/JaIqIEMLXM3bRpkxo1aqRGjRpJkgYOHKhGjRrprbfekru7u3bs2KG7775b119/vXr16qUmTZpo5cqVeaZAmDZtmm644Qa1bdtWnTt31q233qoJEybY1gcHB2vBggU6cuSImjRpopdffllvvfWWevfu7fDXCwAAAAAoORbsW6cXZ7yntGzzbpQNwDl41aup8E/fkMViyXfMubQk9Z35wVX/m5FjzdV///xcUzf9cbUxUQKYOmdumzZtZBj5vxMxf/78K+6jTJky+uGHHwocU79+fa1cyTsXAAAAAICi8fO2hRq1eJKsBfxOC6B0cA8vo/LfjZKbX/43CM3OzdHLs8fpZNKZazqWIUNjl32nzJwsPXtzt2vaF1yTS90ADQAAAAAAs03b8pfeX/Kt2TEAOAGLt5civx0pjwoRBY57d9FEbTnxd5Ed99NVPynXatXzt9xXZPuEa3CpG6ABAAAAAGAmilwAFwsb+6p8mtYtcMz3m//UzJ1Li/zYX6z5RZ+v/qXI9wvnRpkLAAAAAEAh/LBlHkUuAJuQFx9R4EMdCxyz5uh2jVv2fbFl+Grtb/p01U/Ftn84H8pcAAAAAACu4Ict8/TekilmxwDgJPza36Iybz5f4Jij507qtTkfK9ewFmuWr9fN1PgVPxbrMeA8KHMBAAAAACjAjxS5AC7ieUNVRXz1tixu+ddqSRkpemnm+0rOTHVIpkkbfi/WK4DhPChzAQAAAADIx/St8zWaIhfA/3MrE6zy342WW4BfvmNyrLl6dc54RcfHODCZ9O2mufp4JVfolnSUuQAAAAAAXMZvOxZr1OLJZscA4Cw8PRQ5aYQ8q0QVOOyDpVO17thOB4XKa+L63zVlwxxTjg3HoMwFAAAAAOBfFu1fr3cWfmN2DABOJGz0APm2bFTgmF+3L9L0rfMdlOjyPlwxTTN2LjE1A4oPZS4AAAAAABfZEL1bQ/74VFbDMDsKUKQ2ZCfq2cTduuXcBtWIW6WFmWfzrJ+fGaeeibvU9Ow61YhbpT05KYXa75+ZcWofv1l14larc/wWLcs6l2f9N2kn1PzsejU/u17fpJ3Is25bdrLuid+qHCf/+xbUq7uCnri7wDGbju9xmqv531nwjRbtX292DBQDylwAAAAAAP7f3tNH1H/WGGXlZpsdBShy6UauansEaKh/tcuuTzOsauoZpFf9qxR6n1uykzQg+W894B2h2SGNdKdXWb2QtFf7c87f+OvvnFR9lBat8YG19FFgLX2YFq19/78uxzD0ZspBjQioIQ+L5ZpfX3Hxbd1U5d55qcAxJxJi9fLsD5VjzXVQqoLlGlYN/uMT06Z7QPGhzAUAAAAAQFJ0fIz+89topWalmx0FKBatvcpooH9ltfcud9n13XzC9ZLfdWrpGVLofU5JP6nbPEP1rF9F1fDw0wD/yqrjEaDvMk5Jkg7lpqmWh79aeIXoFq8Q3eDhp0O55/+OfZ1+Qs09g1TfM/CaX1tx8axWURHfDJfFwyPfMalZ6eo7830lpCc7MNmVZefmqP+sMdpx8oDZUVCEKHMBAAAAAKXemZR4Pf/rSJ1LSzQ7CuBStuYk6xavkDzLWnmGaGt2kiSplru/juam62Ruhv7JzdCR3HRd7+6nY7np+i3jtAb4VTYhdeG4BQcoctp7cg/Jv2y2GlYNnvuJDp09ke8YM6VnZ6rPjNE6cOa42VFQRChzAQAAAAClWlJGql74dZT+SYw1OwrgcuKsWSrn5pVnWTk3L52xnp+qpIaHn172q6yeSbv1ZNJuveJXRTU8/PRmykEN8q+qldkJ6hS/RV3jt2pDthO9meLurogJQ+VV47oCh41f8aNWHN7ioFBXJykjVS/NeE9xqQlmR0ERyP8acQAAAAAASrjs3BwN/H2cDsRFmx0FKLF6+JZXD9/ytuczMk7L3+KuRp6BujN+s2aGNNSp3Ez1T96npaFN5W0x/9rDskP/I787bipwzJzdKzRl4xwHJbo2p5Lj1HfmB5r00Nvy8fS68gZwWub/7QAAAAAAwCSjFk/WxuO7zY4BuKxybl6Ks2blWRZnzVKYm+dlx5+zZuuTtGi95V9d27KTVdXdV1XcfdXCK0TZhlVHc82fszrw0S4Kef7BAsfsOHlAwxd87aBERWN3zCG98eenMgzD7Ci4BpS5AAAAAIBS6fvNf+q3HYvNjgG4tEYegVqTlZBn2ersBDXyDLrs+HdTD+sp3woq7+4tqwxlX1Qs5spQrswtGn1ubqCw918ucExMUpz6zxqjrNxsB6UqOosObNDHK6ebHQPXgDIXAAAAAFDqrDy8VeOWfW92DMChUo1c7clJ0Z6cFEnScWuG9uSk6GRuhiQpwZqtPTkpOpibJkk6kpuuPTkpOnPRlbevJO/TB6lHbc+f9I3SyuwEfZN2Qody0jQ+9Zh25aTocZ//TatwwaqseB3NTddj/7+unkegDuema3nWOU3PiJG7LKrm7ltcL/+KPK4rr8jJI2TxuvxVxdL5G4r1mzVGZ134ZomTNvyuWTuXmR0DV4k5cwEAAAAApcrBuOMaPPdj5RpWs6MADrUzO1mPJe2yPR+ZekSS1N07XO8HXq/FWec0KOWAbX2/5H2SpJd8K6mff2VJ0sncTLnJYhvT2DNI4wJr6cO0YxqbdkxV3H31RVBtXe/hn+fYGUauhqUe1vjAWnKznN++vLu33gqopkHJB+RlcdP7gdfLx+JePC/+Ciz+vor8bpTcy4XmO8YwDL351+f6O/ao44IVkxELv1aF4DA1u66u2VFgJ8pcAAAAAECpcS4tSX1nfqCULPPn5QQc7WavEB0sd2u+6+/zidB9PhEF7uOHkPqXLOvsXU6dvcsVuJ2PxV0LQ5tcsvwhn0g95BNZ4LbFzmJRxBdvyrtO9QKHfbnmVy3cv95BoYpXjjVXA2eP03c9RqhKmSiz48AOTLMAAAAAACgVsnNzNPD3sfonMdbsKACcSJkhz8i/U6sCxyzYt05frZ3hoESOkZSRqgG/j1NaVobZUWAHylwAAAAAQKnw3pIp2vrPPrNjAHAiAffdqdABTxQ4Zu/pI3rzry9kmHxztuJw+OwJDVswwewYsANlLgAAAACgxJu7Z6V+2b7I7BgAnIh349oK+2hQgWPiUhPUf9YYZeRkOiiV4837e42mbf7T7BgoJMpcAAAAAECJdjDuuN5Z+I3ZMQA4EffyYYr8dqTcfLzzHZOVk60Bs8YqJvmsA5OZY9zyaXxywUVQ5gIAAAAASqy0rAy9MvtDpWeX3KvqANjH4uutyKkj5RFZ8E3bhi2YoB2nDjgolblyrLl6dc5HOpuaYHYUXAFlLgAAAACgxBq2YIKOnDtpdgwATiR8/BD5NLyhwDGT1v+uuXtWOiiRcziTEq/X5oxXjjXX7CgoAGUuAAAAAKBEmr51vub9vcbsGACcSOjAngro1rbAMcsPbdYnq6Y7KJFz2XRir8av+MHsGCgAZS4AAAAAoMTZdeqQxiz7zuwYAJyIf5fWCh3cq8AxB+OOa8gfn8pqGA5K5XymbvpDKw5tMTsG8kGZCwAAAAAoUVIy0/TqnI+UnZtjdhQATsKrbg2Ff/aGLBZLvmPi05LUd+YHSs1Kd2Ay5/T2/K90NjXR7Bi4DMpcAAAAAECJMmrxZJ1MOmN2DABOwj0sVJHfj5abv2++Y7Jzc/TK7A/1T2KsA5M5r3NpiXp7/pdmx8BlUOYCAAAAAEqMBfvWlbqbFgEogJenIqe8K8+KEQUOG7V4sjad2OugUK5h5eGtmr51vtkx8C+UuQAAAACAEiE25ZzeWfiN2TEAOJGwsa/Kp3m9Asf8sGWeftux2EGJXMuHy6fpUNwJs2PgIpS5AAAAAACXZxiG3vzrCyVmpJgdBYCTCP7Pwwp6uFOBY9Ye3aExS6c6KJHrycjJ0pA/PmEOcidCmQsAAAAAcHk/bJ2ndcd2mh0DgJPwa3uzyr79QoFjjp47qVfnjFeuYXVQKte078wxjV/xo9kx8P8ocwEAAAAALu1Q3AmNX/GD2TEAOAnP6ysr4uuhsrjlX3slZaSq36wxSs5MdWAy1/X95j+1IXq32TEgylwAAAAAgAvLzs3R639+qsycbLOjAHACbqFBKv/9e3IL9M93TK7VqtfmjtfRcycdmMy1GTI0fMEEpWdnmh2l1KPMBQAAAAC4rCkb5+jv2KNmxwDgDDzcFTlphDyrVihw2Nhl32nt0R0OClVyHE84rc9X/2x2jFKPMhcAAAAA4JKOnjupCWtnmB0DgJMo924/+d7auMAxM3Ys0bQtfzkoUckzbfNf2nXqkNkxSjXKXAAAAACAyzEMQyMWfqOsXKZXACAFPXWvgp/uVuCYzcf36t1FEx2UqGTKNawaOv8rZefmmB2l1KLMBQAAAAC4nJm7lmrT8T1mxwDgBHxbNVa5kf0KHPNPYqxenj1OOdZcB6UquQ7ERWvSht/NjlFqUeYCAAAAAFzK2dQEfbh8mtkxADgBz6oVFTFxhCweHvmOScvKUL+ZYxSfnuzAZCXb1+tm6vDZf8yOUSpR5gIAAAAAXMp7S75VUkaq2TEAmMwt0F+R34+Se2hQvmOshlVD/vhEB+KiHZis5MvOzdHQ+V/KaljNjlLqUOYCAAAAAFzGikNbNH/fWrNjADCbm5vCJwyV1/VVChz2ycqftOzQZsdkKmW2nzygX7cvNjtGqUOZCwAAAABwCenZmRq5aJLZMQA4gbJvvyD/djcXOOaPPauY27WYfbrqJyUwfYVDUeYCAAAAAFzCxPWzdCo5zuwYAEwW+Ehnhfzn4QLH7Dx1UMMWfOWgRKVXYkaKPl453ewYpQplLgAAAADA6Z1IOK1vN841OwYAk/k0r6ewMa8UOOZ08jkNmDVWmTnZDkpVus3cuUS7Yw6ZHaPUoMwFAAAAADi9Mcu+U1YuxQxQmnlUjFDElHdl8fLMd0xGdpb6zxqjM6nxDkxWulkNQ6MXT5FhGGZHKRUocwEAAAAATm3dsZ1aenCT2TEAmMji56vI70bLIyy0wHFvzvtce04fdlAqXLDj1AH9uXe12TFKBcpcAAAAAIDTyrVa9cHSqWbHAGAmi0Xhn/9X3jfWKHDYl2t+04J96xwUCv/20YoflJaVYXaMEo8yFwAAAADgtGbsXKKDccfNjgHARGUG9VJAl9sKHLNo/wZ9ueZXByXC5cSmnNOkDb+bHaPEo8wFAAAAADillMw0fb76F7NjADBRQLe2Cn25Z4Fj/o49qv/+9ZkMMWer2b7b9IdiU86ZHaNEo8wFAAAAADilSRt+17m0RLNjADCJd8MbFDZ+SIFjzqYmqP/MMUrPznRQKhQkIydLE9bOMDtGiUaZCwAAAABwOmdTEzRt8zyzYwAwiXtkOUV+N0puvt75jsnKydaA38fqVHKcA5PhSmbuXKro+BizY5RYlLkAAAAAAKfzzfpZysjhSjugNLL4eCly6kh5RJYrcNyIhV9r+8kDDkqFwsqx5urz1T+bHaPEoswFAAAAADiVmKQ4/bp9sdkxAJgkbPwQ+TSqXeCYbzfO0ezdKxyUCPaa9/da7Ys9ZnaMEokyFwAAAADgVCasm6Gs3GyzYwAwQUi/xxTYvV2BY1Ye3qqPVvzgoES4GoYMfbJyutkxSiTKXAAAAACA04iOj9Hvu5abHQOACfw7t1KZN3oXOOZQ3AkNnvuxrIbhoFS4WiuPbNWWE3+bHaPEocwFAAAAADiNL9b8qhxrrtkxADiYV93qCv/sv7JYLPmOSUhPVr9ZHyglK92ByXAtxq/80ewIJQ5lLgAAAADAKRyMO655f682OwYAB3MrF6LI70bLLcAv3zE51ly9MvsjHU847cBkuFbb/tmntUd3mB2jRKHMBQAAAAA4hS9W/8JHp4HSxstTkZPekWelyAKHjV48WRuP73ZQKBSlb9bPMjtCiWJqmbtixQp17dpVUVFRslgsmjVrlm1ddna2Bg0apHr16snf319RUVF64okndPLkyTz7qFKliiwWS56v0aNH5xmzY8cOtWrVSj4+PqpUqZLef/99R7w8AAAAAEAhHT13UksObjQ7BgAHC3v/Zfm2aFDgmOlb5+uX7YsclAhFbdPxPdpx8oDZMUoMU8vc1NRUNWjQQJ999tkl69LS0rRlyxa9+eab2rJli2bMmKF9+/bp7rvvvmTs8OHDderUKdvXSy+9ZFuXlJSk9u3bq3Llytq8ebM++OADDR06VBMmTCjW1wYAAAAAKLwpG+dwVS5QygQ/94CCHu1S4Jj1x3bqg6VTHZQIxWUiV+cWGQ8zD96pUyd16tTpsuuCg4O1cOHCPMs+/fRTNW/eXNHR0bruuutsywMDAxUZefnL8adNm6asrCxNmjRJXl5eqlu3rrZt26Zx48apd++C75AIAAAAACh+sSnnNHfPSrNjAHAg3ztuUtlhfQocEx0fo1fmfMRNEUuA5Ye26GDccdUoV8nsKC7P1DLXXomJibJYLAoJCcmzfPTo0RoxYoSuu+469ejRQwMGDJCHx/mXtnbtWt12223y8vKyje/QoYPee+89xcfHKzQ09JLjZGZmKjMz0/Y8KSlJkmS1WmW1WovhlRVO/vdzBApm+dd/AXuY+e8eUJJZrVYZhsHfMbgkfm5R1KZt/kvZuTlmxwDgIJ41Kyvi66GyuLvnOyY5M019Z76vpIxUByZDcTFkaOL63zWqy4tmR3F5LlPmZmRkaNCgQXrkkUcUFBRkW963b181btxYZcqU0Zo1azRkyBCdOnVK48aNkyTFxMSoatWqefYVERFhW3e5MnfUqFEaNmzYJcvPnDmjjIyMonxZdinvEWLaseHaLJJC3f1lkcQH12Cv2NhYsyMAJZLValViYqIMw5CbG/ekhWtJTk42OwJKkOTMNP3KXJhAqeEWEqjI70bJPSgg3zG5VqsGzRmvI+dO5jsGrmf+32vUp+WDqhgSbnYUl+YSZW52drYefPBBGYahL774Is+6gQMH2h7Xr19fXl5eeu655zRq1Ch5e3tf1fGGDBmSZ79JSUmqVKmSwsLC8hTJjnYqJ8G0Y8O1XShxY3ISKHNht/Bw/kcLFAer1SqLxaKwsDDKXLgcHx8fsyOgBPll20KlZKWbHQOAI3i4K+Kb4fKqXvBH7cct/16rj253UCg4Sq5h1bcb5+iNO3uZHcWlOX2Ze6HIPXbsmJYsWXLFMvWmm25STk6Ojh49qlq1aikyMlKnT5/OM+bC8/zm2fX29r5sEezm5mbqL1uUcLhWhvg5gv0omYDiY7FYTD+/AK4GP7MoKlk52Zq25S+zYwBwkHIjXpJf66YFjpm5c6m+3/yngxLB0X7fvUwvtHxAZfzMu1jS1Tn1WdiFIvfAgQNatGiRypYte8Vttm3bJjc3N9uVZC1atNCKFSuUnZ1tG7Nw4ULVqlXrslMsAAAAAAAcY86eFYpLTTA7BgAHCOp5j4Kfua/AMVv/2ad3F010UCKYITMnWzN2LDY7hksztcxNSUnRtm3btG3bNknSkSNHtG3bNkVHRys7O1v333+/Nm3apGnTpik3N1cxMTGKiYlRVlaWpPM3N/voo4+0fft2HT58WNOmTdOAAQP02GOP2YraHj16yMvLS7169dLu3bv1008/afz48XmmUQAAAAAAON4PW+aZHQGAA/i0bKRyo/oXOOZk4hkN/H0cN0MsBX7Zvki53Ez1qpk6zcKmTZt0++23255fKFh79uypoUOHavbs2ZKkhg0b5tlu6dKlatOmjby9vTV9+nQNHTpUmZmZqlq1qgYMGJCnqA0ODtaCBQvUp08fNWnSROXKldNbb72l3r17F/8LBAAAAABc1pYTe3Uw7rjZMQAUM48qUYqcNEIWz/wrqLSsDPWb9YHOpSU6MBnMEpN8VssObVLbms3NjuKSTC1z27RpI8PIfwbPgtZJUuPGjbVu3borHqd+/fpauXKl3fkAAAAAAMXjp20LzY4AoJhZAvxU/vvRci8TnO8YwzD0+p+faf+ZaAcmg9mmb51PmXuVnHrOXAAAAABAyXM2NVGLD2wwOwaA4uTmpoiv3pZXraoFDvts9c9aenCjg0LBWWyI3q3DZ/8xO4ZLoswFAAAAADjUzJ1LmBcTKOHKvPmc/NvfUuCYv/au1tfrZjooEZzN9K3zzY7gkihzAQAAAAAOYzWs+o07mQMlWsCDHRX6Yo8Cx+w6dUhvz//SQYngjObsXqGUzDSzY7gcylwAAAAAgMOsOLxVJ5PizI4BoJh4N7tR4eNeLXBMbMo59f99jDJzsh2UCs4oLTtDc3avMDuGy6HMBQAAAAA4zC/c+AwosTwqhCtyyruyeHvlOyYjO0v9Z43VmZR4ByaDs5q5a5nZEVwOZS4AAAAAwCFOJcVpzdHtZscAUAwsfj6K/G60PMLLFDhu6PwvtTvmkINSwdntiz2q/WeOmR3DpVDmAgAAAAAc4o89K2U1DLNjAChqFovCP3ld3vVqFjjs63Uz9dffaxwUCq5i9i6mWrAHZS4AAAAAwCH+2LPK7AgAikHoq08p4O7bCxyz5MBGfbbqZwclgiv5c+8q5VhzzY7hMihzAQAAAADFbk/MYR0+94/ZMQAUMf977lDoK08WOGZf7DG9/uenMsSV+bjU2bRErTnCFDyFRZkLAAAAACh2c/esNDsCgCLm3aCWwj95XRaLJd8x59KS1H/WB0rPznRgMriaObuZaqGwKHMBAAAAAMUqx5rLPJlACeMeXkaRU0fKzdc73zHZuTka+PtYnUyKc2AyuKJlhzYrKSPF7BgugTIXAAAAAFCs1h7doXNpiWbHAFBELD5eipw6Uh5R4QWOe2fhN9r6zz4HpYIry8rN1ry/15odwyVQ5gIAAAAAihVTLAAlS9i41+TTpG6BY6Zu+kOzdi1zTCCUCH/w/4pCocwFAAAAABSb1Kx0LTu4yewYAIpIyEuPKvCBDgWOWXV4qz5aPs1BiVBSbD95QGdS4s2O4fTsLnOPHz+uEydO2J5v2LBB/fv314QJE4o0GAAAAADA9a04vFUZOVlmxwBQBPw6tFSZ//YucMyRs/9o8B+fKNewOigVSgpDhpYc2Gh2DKdnd5nbo0cPLV26VJIUExOjO++8Uxs2bNAbb7yh4cOHF3lAAAAAAIDrWnJgg9kRABQBr9rVFPHlW7K45V8lJaanqO/MD5ScmebAZChJFh1Yb3YEp2d3mbtr1y41b95ckvTzzz/rxhtv1Jo1azRt2jRNmTKlqPMBAAAAAFxUVk62Vh/ZbnYMANfIrWywIr8bJbcAv3zH5Fhz9eqcjxSdEOPAZChptpz4WwnpyWbHcGp2l7nZ2dny9vaWJC1atEh33323JOmGG27QqVOnijYdAAAAAMBlrY/epdSsdLNjALgWnh6KnPSOPCtHFTjs/SXfan30LgeFQkmVY83VUuZZL5DdZW7dunX15ZdfauXKlVq4cKE6duwoSTp58qTKli1b5AEBAAAAAK5p6UHmPgRcXdh7A+R7S8MCx/y8baF+2rbAMYFQ4i1mep4C2V3mvvfee/rqq6/Upk0bPfLII2rQoIEkafbs2bbpFwAAAAAApZvVsGrZwc1mxwBwDYKfuU9Bj99d4JgN0bv13pIpjgmEUmH9sV1KYd7lfHnYu0GbNm0UFxenpKQkhYaG2pb37t1bfn75z50CAAAAACg9tp88oLNpiWbHAHCVfNs0U9l3XipwzPGEGL0y+0PlWHMdlAqlQVZutlYe3qpOtVuaHcUp2X1lriQZhqHNmzfrq6++UnLy+UmJvby8KHMBAAAAAJKkpQeYYgFwVZ7VKynim2GyuLvnOyYlM019Z45RYkaKA5OhtFh+iE925MfuK3OPHTumjh07Kjo6WpmZmbrzzjsVGBio9957T5mZmfryyy+LIycAAAAAwIUs4xdxwCW5BQcoctpouQcH5jvGalg1eO4nOnz2hAOToTRZd2yXDMOQxWIxO4rTsfvK3H79+qlp06aKj4+Xr6+vbXm3bt20ePHiIg0HAAAAAHA9/yTG6lj8KbNjALCXu7sivhkur+rXFTjso+U/aOWRrQ4KhdIoPj1Jf8ceNTuGU7L7ytyVK1dqzZo18vLyyrO8SpUq+ueff4osGAAAAADANa07ttPsCACuQtlhfeTXplmBY2bvWq5vN811UCKUZmuOblftiKpmx3A6dl+Za7ValZt76cTWJ06cUGBg/pfgAwAAAABKB8pcwPUEPt5VIc89UOCYbf/s04iF3zgoEUq7NUd3mB3BKdld5rZv314fffSR7bnFYlFKSorefvttde7cuSizAQAAAABcjGEY2hi92+wYAOzg06KBwt4bWOCYU0lxGvj7OGXlZjsoFUq77Sf3Ky0rw+wYTsfuMnfs2LFavXq16tSpo4yMDPXo0cM2xcJ7771XHBkBAAAAAC7i79ijik9PNjsGgELyqFxekZPfkcUz/5k407Iy1G/WGJ1NS3RgMpR22bk52nicNwf/ze45cytWrKjt27dr+vTp2rFjh1JSUtSrVy89+uijeW6IBgAAAAAofdYf22V2BACFZAnwU+R3o+VeNiTfMYZh6L9/fa593IwKJlh7dKdaV29idgynYneZK0keHh567LHHijoLAAAAAMDFrYtmvlzAJbi5KeLLt+Rdu1qBwz5f/YsWH9jgoFBAXmuObjc7gtMpVJk7e/bsQu/w7rvvvuowAAAAAADXlZWTra0n9pkdA0AhlHn9Wfl3aFngmHl/r9GEdTMclAi41LH4UzqbmqCy/iFmR3EahSpz77333kLtzGKxKDc391ryAAAAAABc1M5TB5WRk2l2DABXEPBAe4X2K/gT13tiDuvteV86KBGQv20n96ttzeZmx3AahboBmtVqLdQXRS4AAAAAlF7bTu43OwKAK/BuUkfhHw4qcMyZlHj1mzVGGTlZDkoF5G/bP3zi42KFKnMBAAAAALiSnacOmB0BQAHco8IV+e1IWby98h2TmZOl/rPGKDblnAOTAfnbzhuFeVxVmbt48WLdddddql69uqpXr6677rpLixYtKupsAAAAAAAXsuMkZS7grCx+Pio/daQ8IsoWOG7o/AnaFXPIQamAK9t7+qiycrLNjuE07C5zP//8c3Xs2FGBgYHq16+f+vXrp6CgIHXu3FmfffZZcWQEAAAAADi5EwmxOpuWaHYMAJdjsSj849fl3aBWgcMmrp+lP/euclAooHCycrO1+/Rhs2M4jULdAO1iI0eO1IcffqgXX3zRtqxv375q2bKlRo4cqT59+hRpQAAAAACA82OKBcB5hb7cUwH33F7gmGUHN+mTlT85KBFgn+3/7FejCgW/GVFa2H1lbkJCgjp27HjJ8vbt2ysxkXdhAQAAAKA02s4UC4BT8r+rtUJfe7rAMQfORGvIH5/KkOGgVIB9tp3kJmgX2F3m3n333Zo5c+Yly3///XfdddddRRIKAAAAAOBadnBlLuB0vG6sqfDP/iuLxZLvmPi0JPWd+YHSsjMcmAywD28Y/o/d0yzUqVNH7777rpYtW6YWLVpIktatW6fVq1fr5Zdf1scff2wb27dv36JLCgAAAABwSpk5WdoXe9TsGAAu4h5WRuW/HyU3P598x2Tn5mjg7HE6mXTGgckA+51LS1RMUpwig8qZHcV0dpe5EydOVGhoqPbs2aM9e/bYloeEhGjixIm25xaLhTIXAAAAAEqBfbHHlGPNNTsGgP9n8fZS5LfvyqNCRIHj3l00UVtO/O2gVMC1+Tv2KGWurqLMPXLkSHHkAAAAAAC4qH1njpkdAcBFwsa+Kp9mNxY4ZtrmPzVz51IHJQKu3d+xx9SmRlOzY5jO7jlzAQAAAAC42P5YylzAWQT3eViBD1164/qLrTm6XWOXfe+gREDR+JvpfCRdxZW5hmHo119/1dKlSxUbGyur1Zpn/YwZM4osHAAAAADA+e3nylzAKfjd2UJl33qhwDFHz53Ua3M+Vq5hLXAc4Gz4f815dl+Z279/fz3++OM6cuSIAgICFBwcnOcLAAAAAFB6GIahA3HHzY4BlHqeN1RVxIShsrjlX/UkZaTopZnvKzkz1YHJgKJxMvGM0rIyzI5hOruvzP3uu+80Y8YMde7cuTjyAAAAAABcyKmkOKVmpZsdAyjV3MoEq/x3o+UW4JfvmBxrrl6dM17R8TEOTAYUHUOGDsYdV/2ommZHMZXdV+YGBwerWrVqxZEFAAAAAOBiDp09YXYEoHTz9FDkpBHyrBJV4LAxS7/TumM7HRQKKB4H4qLNjmA6u8vcoUOHatiwYUpP551XAAAAACjtDsVR5gJmKjeqv3xbNipwzK/bF+nHrfMclAgoPgfOMK2P3dMsPPjgg/rxxx8VHh6uKlWqyNPTM8/6LVu2FFk4AAAAAIBzO3SWX6wBswQ93V3BPe8pcMym43s0avFkByUCild0/CmzI5jO7jK3Z8+e2rx5sx577DFFRETIYrEURy4AAAAAgAs4fPYfsyMApZJv66Yq9+5LBY45kRCrl2d/qBxrroNSAcXreMJpsyOYzu4y948//tD8+fN16623FkceAAAAAIAL+Scx1uwIQKnjWa2iIr4ZLotH/rVOala6+s58XwnpyQ5MBhSvk0lnlGu1yt3N7pljSwy7X3mlSpUUFBRUHFkAAAAAAC4kLStD8RRFgEO5BQUo8vvRcg8JzHeM1bBqyB+fcoNClDg51lydSoozO4ap7C5zx44dq9dee01Hjx4thjgAAAAAAFfBVbmAg7m7K2LCUHnVrFzgsPErftTyQ5sdFApwrBOJpXuqBbunWXjssceUlpam6tWry8/P75IboJ07d67IwgEAAAAAnBdlLuBYZYe+IL+2NxU4Zs7uFZqycY6DEgGOFx0fo5sr1zM7hmnsLnM/+uijYogBAAAAAHA1JyhzAYcJ7NFFIc8/VOCYHScPaPiCrx2UCDDHiVJ+EzS7y9yePXsWRw4AAAAAgIvhylzAMXxuqq+wD14ucMzp5LMa8PtYZeVmOygVYI7jTLNw9TIyMpSVlZVnGTdHAwAAAIDS4Z/EM2ZHAEo8j0qRipzyjixenvmOSc/OVL9ZYxSXmuC4YIBJSvuVuXbfAC01NVUvvviiwsPD5e/vr9DQ0DxfAAAAAIDSgStzgeJl8fdV5Hej5F4u/77FMAy9+dfn2nv6iAOTAeY5nVy679dld5n72muvacmSJfriiy/k7e2tb775RsOGDVNUVJSmTp1aHBkBAAAAAE7oVFKc2RGAkstiUcTn/5V33RoFDvtyza9auH+9g0IB5kvKSFV2bo7ZMUxjd5k7Z84cff7557rvvvvk4eGhVq1a6b///a9GjhypadOm2bWvFStWqGvXroqKipLFYtGsWbPyrDcMQ2+99ZbKly8vX19ftWvXTgcOHMgz5ty5c3r00UcVFBSkkJAQ9erVSykpKXnG7NixQ61atZKPj48qVaqk999/396XDQAAAAC4SHp2plKz0s2OAZRYZQY/I//OtxU4ZuG+dfpq7QwHJQKcgyFDZ0vxlCJ2l7nnzp1TtWrVJJ2fH/fcufOXNt96661asWKFXftKTU1VgwYN9Nlnn112/fvvv6+PP/5YX375pdavXy9/f3916NBBGRkZtjGPPvqodu/erYULF2ru3LlasWKFevfubVuflJSk9u3bq3Llytq8ebM++OADDR06VBMmTLD3pQMAAAAA/t+5tESzIwAlVmD3OxU68IkCx+w9fUT//esLGTIclApwHnGppff/QXbfAK1atWo6cuSIrrvuOt1www36+eef1bx5c82ZM0chISF27atTp07q1KnTZdcZhqGPPvpI//3vf3XPPfdIkqZOnaqIiAjNmjVLDz/8sPbu3at58+Zp48aNatq0qSTpk08+UefOnTVmzBhFRUVp2rRpysrK0qRJk+Tl5aW6detq27ZtGjduXJ7S92KZmZnKzMy0PU9KSpIkWa1WWa1Wu15jUbKYdmS4Osu//gvYw8x/94CSzGq1yjAM/o7BJfFzC0k6l5ZkdgSgxAod3KvA9XGpCeo/a4wycjILHAeUVHFpCWZHMI3dZe5TTz2l7du3q3Xr1ho8eLC6du2qTz/9VNnZ2Ro3blyRBTty5IhiYmLUrl0727Lg4GDddNNNWrt2rR5++GGtXbtWISEhtiJXktq1ayc3NzetX79e3bp109q1a3XbbbfJy8vLNqZDhw567733FB8ff9mbto0aNUrDhg27ZPmZM2fyXBXsaOU9Qkw7NlybRVKou78sEu/Zwm6xsdzYBCgOVqtViYmJMgxDbm52f1gKMFVycrLZEeAE4ilzgWJjseR/KU5WTrYGzBqrmOSzDkwEOJfSPM2C3WXugAEDbI/btWunvXv3asuWLapRo4bq169fZMFiYmIkSREREXmWR0RE2NbFxMQoPDw8z3oPDw+VKVMmz5iqVateso8L6y5X5g4ZMkQDBw60PU9KSlKlSpUUFhamoKCga3xlV+9UToJpx4Zru1DixuQkUObCbv/+dxZA0bBarbJYLAoLC6PMhcvx8fExOwKcAFfmAuYYtmCCdpw6cOWBQAl2lmkWrl6VKlVUpUqVIojiPLy9veXt7X3Jcjc3N1N/2aKEw7UyxM8R7EfJBBQfi8Vi+vkFcDX4mYXEnLmAGSZvmK25e1aaHQMwXVwpvjK30Gdha9eu1dy5c/Msmzp1qqpWrarw8HD17t07zzyz1yoyMlKSdPr06TzLT58+bVsXGRl5ycd/c3JydO7cuTxjLrePi48BAAAAALAPV+YCjrX80GZ9vPJHs2MATqE0T7NQ6DJ3+PDh2r17t+35zp071atXL7Vr106DBw/WnDlzNGrUqCILVrVqVUVGRmrx4sW2ZUlJSVq/fr1atGghSWrRooUSEhK0efNm25glS5bIarXqpptuso1ZsWKFsrOzbWMWLlyoWrVqXXaKBQAAAADAlZ1L58pcwFEOxh3XkD8+ldXgs5aAJCVlppodwTSFLnO3bdumtm3b2p5Pnz5dN910k77++msNHDhQH3/8sX7++We7Dp6SkqJt27Zp27Ztks7f9Gzbtm2Kjo6WxWJR//799c4772j27NnauXOnnnjiCUVFRenee++VJNWuXVsdO3bUs88+qw0bNmj16tV68cUX9fDDDysqKkqS1KNHD3l5ealXr17avXu3fvrpJ40fPz7PnLgAAAAAAPskpHEjPMAR4tOS1HfmB0rNSjc7CuA0UrMyzI5gmkLPmRsfH5/nZmTLly9Xp06dbM+bNWum48eP23XwTZs26fbbb7c9v1Cw9uzZU1OmTNFrr72m1NRU9e7dWwkJCbr11ls1b968PDdcmDZtml588UW1bdtWbm5uuu+++/Txxx/b1gcHB2vBggXq06ePmjRponLlyumtt95S79697coKAAAAAPif1OzS+4s04CjZuTl6ZfaH+icx9sqDgVIknTL3yiIiInTkyBFVqlRJWVlZ2rJli4YNG2Zbn5ycLE9PT7sO3qZNGxkFfETAYrFo+PDhGj58eL5jypQpox9++KHA49SvX18rVzJBOAAAAAAUlYzsortnCoDLG7V4sjad2Gt2DMDppGaX3ivVCz3NQufOnTV48GCtXLlSQ4YMkZ+fn1q1amVbv2PHDlWvXr1YQgIAAAAAnEs6ZS5QrH7YMk+/7Vh85YFAKZTGlblXNmLECHXv3l2tW7dWQECAvv32W3l5ednWT5o0Se3bty+WkAAAAAAA50KZCxSftUd3aMzSqWbHAJwWZW4hlCtXTitWrFBiYqICAgLk7u6eZ/0vv/yigICAIg8IAAAAAHA+lLlA8Th67qRenTNeuYbV7CiA08o1rMrIzpKPp9eVB5cwhS5zLwgODr7s8jJlylxzGAAAAACAa8jIocwFilpSRqr6zRqj5MxUs6MATi81K71UlrmFnjMXAAAAAABJyrHmKjs3x+wYQImSa7Vq0NyPdfTcSbOjAC4hrZTeBI0yFwAAAABgF6ZYAIre2GXfac3R7WbHAFxGVk7pfFORMhcAAAAAYJfMnCyzIwAlyowdSzRty19mxwBcitUwzI5gikKVuY0bN1Z8fLwkafjw4UpLSyvWUAAAAAAA55Vr5cZMQFHZfHyv3l000ewYgAuizM3X3r17lZp6fvLtYcOGKSUlpVhDAQAAAACcl8VidgKgZPgnMVYvzx6nHGuu2VEAl1Nar8z1KMyghg0b6qmnntKtt94qwzA0ZswYBQQEXHbsW2+9VaQBAQAAAAAASpq0rAz1mzlG8enJZkcBXJJRSq/MLVSZO2XKFL399tuaO3euLBaL/vrrL3l4XLqpxWKhzAUAAACAEs4iLs0FrtV///pcB+KizY4BuCyDK3PzV6tWLU2fPl2S5ObmpsWLFys8PLxYgwEAAAAAAJRU647tNDsC4NIocwvJykT3AAAAAFCqWZg0FwBgMubMtcOhQ4f00Ucfae/evZKkOnXqqF+/fqpevXqRhgMAAAAAOB+qXACA2UrrnLlu9m4wf/581alTRxs2bFD9+vVVv359rV+/XnXr1tXChQuLIyMAAAAAAAAA2LiV0k+J2H1l7uDBgzVgwACNHj36kuWDBg3SnXfeWWThAAAAAADOqHT+Ag0AcB4+Ht5mRzCF3Vfm7t27V7169bpk+dNPP609e/YUSSgAAAAAgPPy9vA0OwIAoJTz8fQyO4Ip7C5zw8LCtG3btkuWb9u2TeHh4UWRCQAAAADgxPy8fMyOAAAo5Xw8SmeZa/c0C88++6x69+6tw4cP65ZbbpEkrV69Wu+9954GDhxY5AEBAAAAAM7FzeImHw8vZeRkmR0FAFBK+XiWzmkW7C5z33zzTQUGBmrs2LEaMmSIJCkqKkpDhw5V3759izwgAAAAAMD5+Hp6U+YCAExTWufMtbvMtVgsGjBggAYMGKDk5GRJUmBgYJEHAwAAAAA4L19PH8WnJ5sdAwBQCnm6e8jdze7ZY0sEu8vci1HiAgAAAEDpxLy5AACzlNb5cqWruAEaAAAAAAC+pXSuQgCA+UrrFAsSZS4AAAAA4Cr4eXJlLgDAHKX50yGUuQAAAAAAu/l5+ZodAQBQSoX6BZkdwTR2lbnZ2dlq27atDhw4UFx5AAAAAAAuINjH3+wIAIBSKtSXMrdQPD09tWPHjuLKAgAAAABwEWX9g82OAAAopcr4BZodwTR2T7Pw2GOPaeLEicWRBQAAAADgIsr4UeYCAMwR4lt6y1wPezfIycnRpEmTtGjRIjVp0kT+/nk/WjNu3LgiCwcAAAAAcE5lKXMBACYpzdMs2F3m7tq1S40bN5Yk7d+/P886i8VSNKkAAAAAAE6tDNMsAABMUppvgGZ3mbt06dLiyAEAAAAAcCFcmQsAMEtpvjLX7jlzLzh48KDmz5+v9PR0SZJhGEUWCgAAAADg3ChzAQBmKVOKr8y1u8w9e/as2rZtq+uvv16dO3fWqVOnJEm9evXSyy+/XOQBAQAAAADOJ8Q3UB5u7mbHAACUQqX5Jpx2l7kDBgyQp6enoqOj5efnZ1v+0EMPad68eUUaDgAAAADgnCwWS6n+mCsAwBye7h4KCwgxO4Zp7J4zd8GCBZo/f74qVqyYZ3nNmjV17NixIgsGAAAAAHBu4YFldCY13uwYAIBSJDKwrNwsVz1zrMuz+5WnpqbmuSL3gnPnzsnb27tIQgEAAAAAnF/F4HCzIwAASpmooDCzI5jK7jK3VatWmjp1qu25xWKR1WrV+++/r9tvv71IwwEAAAAAnFfFEMpcAIBjVSjlbyTaPc3C+++/r7Zt22rTpk3KysrSa6+9pt27d+vcuXNavXp1cWQEAAAAADihisERZkcAAJQyUcFcmWuXG2+8Ufv379ett96qe+65R6mpqerevbu2bt2q6tWrF0dGAAAAAIATqhhCmQsAcKzSPs2C3VfmSlJwcLDeeOONos4CAAAAAHAhzJkLAHC00n5l7lWVufHx8Zo4caL27t0rSapTp46eeuoplSlTpkjDAQAAAACcV2RQWXm4uSvHmmt2FABAKVHar8y1e5qFFStWqEqVKvr4448VHx+v+Ph4ffzxx6patapWrFhRHBkBAAAAAE7IzeJW6n+pBgA4jreHp8IDQs2OYSq7r8zt06ePHnroIX3xxRdyd3eXJOXm5uo///mP+vTpo507dxZ5SAAAAACAc6oQEq7ohBizYwAASoEqoVGyWCxmxzCV3VfmHjx4UC+//LKtyJUkd3d3DRw4UAcPHizScAAAAAAA51a1TJTZEQAApUTVshXMjmA6u8vcxo0b2+bKvdjevXvVoEGDIgkFAAAAAHAN1ctWNDsCAKCUqEaZW7hpFnbs2GF73LdvX/Xr108HDx7UzTffLElat26dPvvsM40ePbp4UgIAAAAAnFL1cpXMjgAAKCV4A7GQZW7Dhg1lsVhkGIZt2WuvvXbJuB49euihhx4qunQAAAAAAKdWgzIXAOAglLmFLHOPHDlS3DkAAAAAAC4o0NtPkYFlFZN81uwoAIASzMfDS9eFljc7hukKVeZWrly5uHMAAAAAAFzU9WGVKXMBAMWqWtmKcnez+/ZfJU6hytx/O3nypFatWqXY2FhZrdY86/r27VskwQAAAAAArqFWeGWtOLzF7BgAgBKMaX3Os7vMnTJlip577jl5eXmpbNmyslgstnUWi4UyFwAAAABKmVphfJoTAFC8bgjn/zXSVZS5b775pt566y0NGTJEblzaDAAAAAClXq3wKmZHAACUcDeWr2F2BKdgdxublpamhx9+mCIXAAAAACBJqhQSoSAff7NjAABKKA83d93AG4eSrqLM7dWrl3755ZfiyAIAAAAAcEEWi0X1IrliCgBQPGqFVZa3h5fZMZyC3dMsjBo1SnfddZfmzZunevXqydPTM8/6cePGFVk4AAAAAIBraFDheq0+ut3sGACAEqheVE2zIziNqypz58+fr1q1aknSJTdAAwAAAACUPg2irjc7AgCghKrHfLk2dpe5Y8eO1aRJk/Tkk08WQxwAAAAAgCuqV76G3CwWWQ3D7CgAgBKGMvd/7J4z19vbWy1btiyOLAAAAAAAF+Xv5asa5SqZHQMAUMIE+wSocmh5s2M4DbvL3H79+umTTz4pjiwAAAAAABdWn6kWAABFjKty87K7zN2wYYO+/fZbVatWTV27dlX37t3zfBW1KlWqyGKxXPLVp08fSVKbNm0uWff888/n2Ud0dLS6dOkiPz8/hYeH69VXX1VOTk6RZwUAAACA0qwhZS4AoIjV5+Znedg9Z25ISEixlLb52bhxo3Jzc23Pd+3apTvvvFMPPPCAbdmzzz6r4cOH2577+fnZHufm5qpLly6KjIzUmjVrdOrUKT3xxBPy9PTUyJEjHfMiAAAAAKAU4BduAEBRa37djWZHcCp2l7mTJ08ujhz5CgsLy/N89OjRql69ulq3bm1b5ufnp8jIyMtuv2DBAu3Zs0eLFi1SRESEGjZsqBEjRmjQoEEaOnSovLy8LtkmMzNTmZmZtudJSUmSJKvVKqvVWhQv66pYTDsyXJ3lX/8F7GHmv3tASWa1WmUYBn/H4JL4uUV+KoeWV1hAqM6kxJsdBQBQAvh5+ujGyOpmx3Aqdpe5ZsrKytL333+vgQMHymL5Xy01bdo0ff/994qMjFTXrl315ptv2q7OXbt2rerVq6eIiAjb+A4dOuiFF17Q7t271ahRo0uOM2rUKA0bNuyS5WfOnFFGRkYxvLLCKe8RYtqx4doskkLd/WWRxL2FYa/Y2FizIwAlktVqVWJiogzDkJub3TNfAaZKTk42OwKc2M3X1dOcPSvMjgEAKAGaVKwtT3eXqi+Lnd3fjapVq+YpUv/t8OHD1xSoILNmzVJCQoKefPJJ27IePXqocuXKioqK0o4dOzRo0CDt27dPM2bMkCTFxMTkKXIl2Z7HxMRc9jhDhgzRwIEDbc+TkpJUqVIlhYWFKSgoqIhfVeGdykkw7dhwbRdK3JicBMpc2C08PNzsCECJZLVaZbFYFBYWRpkLl+Pj42N2BDixFlXqU+YCAIpE88pMsfBvdpe5/fv3z/M8OztbW7du1bx58/Tqq68WVa7Lmjhxojp16qSoqCjbst69e9se16tXT+XLl1fbtm116NAhVa9+dZdhe3t7y9vb+5Llbm5upv6yRQmHa2WInyPYj5IJKD4Wi8X08wvgavAzi4LcXPlGWWSRwZknAOAa3UyZewm7y9x+/fpddvlnn32mTZs2XXOg/Bw7dkyLFi2yXXGbn5tuukmSdPDgQVWvXl2RkZHasGFDnjGnT5+WpHzn2QUAAAAAXJ2y/iGqGVZJ+89Emx0FAODCQn2DVLPcdWbHcDpF9pZ6p06d9NtvvxXV7i4xefJkhYeHq0uXLgWO27ZtmySpfPnykqQWLVpo586deeZ8XLhwoYKCglSnTp1iywsAAAAApdXNleuZHQEA4OKaX1e3wKleS6siK3N//fVXlSlTpqh2l4fVatXkyZPVs2dPeXj872LiQ4cOacSIEdq8ebOOHj2q2bNn64knntBtt92m+vXrS5Lat2+vOnXq6PHHH9f27ds1f/58/fe//1WfPn0uO5UCAAAAAODa3Fy5vtkRAAAu7iamWLgsu6dZaNSoUZ5W3DAMxcTE6MyZM/r888+LNNwFixYtUnR0tJ5++uk8y728vLRo0SJ99NFHSk1NVaVKlXTffffpv//9r22Mu7u75s6dqxdeeEEtWrSQv7+/evbsqeHDhxdLVgAAAAAo7ZpUrC0vd09l5WabHQUA4KJa8MbgZdld5t577715nru5uSksLExt2rTRDTfcUFS58mjfvr0M49LJ8ytVqqTly5dfcfvKlSvrzz//LI5oAAAAAIB/8fH0UqMKtbQ+epfZUQAALqhWWGVFBYeZHcMp2V3mvv3228WRAwAAAABQgrSu3pgyFwBwVdrUaGp2BKdVZHPmAgAAAABwwR01m5sdAQDgom6nzM1XoctcNzc3ubu7F/h18c3JAAAAAAClV/mgcqoTUc3sGAAAF1M+sJxqR1Q1O4bTKnT7OnPmzHzXrV27Vh9//LGsVmuRhAIAAAAAuL47ajbTntOHzY4BAHAhrWs0MTuCUyt0mXvPPfdcsmzfvn0aPHiw5syZo0cffVTDhw8v0nAAAAAAANd1R41m+nTVT2bHAAC4EKZYKNhVzZl78uRJPfvss6pXr55ycnK0bds2ffvtt6pcuXJR5wMAAAAAuKjq5SqqSpkos2MAAFxEoLe/mlaqY3YMp2ZXmZuYmKhBgwapRo0a2r17txYvXqw5c+boxhtvLK58AAAAAAAXdkeNZmZHAAC4iFbVGsrDzd3sGE6t0GXu+++/r2rVqmnu3Ln68ccftWbNGrVq1ao4swEAAAAAXFzbms3NjgAAcBF3Xn+z2RGcXqHnzB08eLB8fX1Vo0YNffvtt/r2228vO27GjBlFFg4AAAAA4NrqRlZT+cByOpUcZ3YUAIATC/YJUKtqjcyO4fQKXeY+8cQTslgsxZkFAAAAAFDCWCwWdardUpM2/G52FACAE7uz1s3ydC90VVlqFfo7NGXKlGKMAQAAAAAoqbrWvY0yFwBQoC61W5odwSXYdQM0AAAAAADsVa1sBdWNqGZ2DACAk4oKKqdGFW4wO4ZLoMwFAAAAABS7u+reZnYEAICT6lS7JdO7FhJlLgAAAACg2HW64RZ5uLmbHQMA4IS61G5ldgSXQZkLAAAAACh2oX5BurVqQ7NjAACcTK3wKqperqLZMVwGZS4AAAAAwCG6MtUCAOBf7qrDVbn2oMwFAAAAADhE6+pNFOTjb3YMAICT8HL31N280WcXylwAAAAAgEN4untwBRYAwObOWjcpxDfQ7BguhTIXAAAAAOAwDza40+wIAAAn8QD/T7AbZS4AAAAAwGGqlq2gm6670ewYAACT1Sx3nRpVqGV2DJdDmQsAAAAAcKgHG7Y3OwIAwGQPNGhndgSXRJkLAAAAAHCo22s0VURgGbNjAABM4ufpo7vqMof61aDMBQAAAAA4lLubm+6vzxVZAFBadardUv5evmbHcEmUuQAAAAAAh7uv/h3ycHM3OwYAwAQPNuTGZ1eLMhcAAAAA4HBl/UPU7vqbzI4BAHCwxhVv0A3hVcyO4bIocwEAAAAApnikUQezIwAAHKxns65mR3BplLkAAAAAAFM0rFBLDSvUMjsGAMBBqpWtqNbVGpsdw6VR5gIAAAAATPN087vNjgAAcJCeTe+SxWIxO4ZLo8wFAAAAAJjmtmqNVbPcdWbHAAAUs7CAUHWpc6vZMVweZS4AAAAAwDQWi0VPNmf+RAAo6R5t3Eme7h5mx3B5lLkAAAAAAFN1vOEWRQWFmR0DAFBMArx89UCDdmbHKBEocwEAAAAApvJwc9cTTbuYHQMAUEzub9BOAd5+ZscoEShzAQAAAACm61bvDpXxCzY7BgCgiHl7eOrRJp3MjlFiUOYCAAAAAEzn4+mlRxt3NDsGAKCIPdCgncIDypgdo8SgzAUAAAAAOIVHGndUqG+g2TEAAEXE19NbTze/1+wYJQplLgAAAADAKfh7+eqp5veYHQMAUEQeadRRZf2ZQqcoUeYCAAAAAJzGw43a83FcACgBArx89WSzrmbHKHEocwEAAAAATsPbw0u9W3Q3OwYA4Bo91qSzgn0DzI5R4lDmAgAAAACcSrd6t6tSSITZMQAAVynYJ0CPNe1idowSiTIXAAAAAOBUPNzc9cItD5gdAwBwlXo2u0uB3n5mxyiRKHMBAAAAAE6nU+1bVLPcdWbHAADYqaxfsB5p1NHsGCUWZS4AAAAAwOm4Wdz04q0Pmh0DAGCnPrc+KD8vH7NjlFiUuQAAAAAAp9SmRlM1rVjb7BgAgEKqWe463Xvj7WbHKNEocwEAAAAATuuV25+Qm8VidgwAQCG83OYxubtRNxYnvrsAAAAAAKdVO6Kq7rmxjdkxAABXcGvVhmpRpb7ZMUo8ylwAAAAAgFN76daH5O/la3YMAEA+PNzc9ertT5gdo1SgzAUAAAAAOLWy/iF6rkV3s2MAAPLxSKOOqlImyuwYpQJlLgAAAADA6fVo3ElVKQoAwOmU8QvWc7fcZ3aMUoMyFwAAAADg9DzdPfTaHT3NjgEA+Je+rR5WoLef2TFKDcpcAAAAAIBLuKVKA91eo5nZMQAA/69pxdq6l5tUOhRlLgAAAADAZQy+oyc3QwMAJ+Dl7qk32z8ri8VidpRShTIXAAAAAOAyIoPK6aVbHzI7BgCUer1uuoebnpmAMhcAAAAA4FIeatReDaOuNzsGAJRaVctEqddN95odo1SizAUAAAAAuBQ3i5veat9bnu4eZkcBgFLHIovevPNZ/g02Cd91AAAAAIDLqV6uono1v0dfrv3N7Ci4BobVqtMLdit+yzHlJGfIM8hHoU2rKrxdHds8nNnJGYr5Y7uSD8QoNz1b/lXDVOHexvIOCyxw3wnbj+v0/J3Kik+Vd7lARXaur6Da//tI+Jllfyt22d+SpPDbb1BY6xts69Kiz+qfGZtV46V2srhzHRxwsW71bleTSrXNjlFq8S8SAAAAAMAlPXNzN1UrW9HsGLgGZ5b+rbNrD6pCt8aq9WonRXZpoDPL/9bZ1QckSYZh6NiUVco6l6oqT96qmv3byyvUT4cnLJM1Kyff/aYejVP0D2sV2ryaavbvoKC6FXTs29XKiEmQJKWfTFDMgl2q/FgLXfdoC8XM26X0U+fXGblWnfhtkyrc14QiF/iXsn7BGtC6h9kxSjX+VQIAAAAAuCRPdw8N6/Cc3LiTustKPRanoLoVFFQ7Sl5l/BVSv5ICakYqLfqcJCkrLkVp0WdVoXsT+VUqK5/wIFXo3lTW7FzFbz2W737jVu1XYK1Ihbe5QT4RQYrsWE++FUIUt/qgJCnzTJJ8ywcroEaEAmtGyKd8sDJjkyVJZ5b/Lf9qYfKrVLb4vwGAi3m93dMK8gkwO0apRpkLAAAAAHBZ9aNq6uFGHcyOgavkX7mcUg6eVuaZ80Vq+sl4pR09o8AbIiVJ1pxcSZLFw922jcXNIjcPN6Udict3v2nHziqgZkSeZQHXl1fasfPb+ESGKPNMirLiU89/xSXLJzJYmXEpOrfxiCI71CvS1wmUBHfVaaV2199kdoxSjzlzAQAAAAAurV+rHlp3bJcOnz1hdhTYKez22srNzNa+D/6ULBbJMBTZsZ5CG1eRJPmEB8kzxE8xf+1Qhfuays3LXXEr9ys7MV3ZyRn57jcnOUMeAT55lnkEeivn/7fxiQhSZKd6Ovz1cklSZKf68okI0uGvlql8lwZK3h+j0wt2yeLupqh7GimgWnjxfAMAF1E+sJyGtH3K7BiQk1+ZO3ToUFksljxfN9zwvwnJMzIy1KdPH5UtW1YBAQG67777dPr06Tz7iI6OVpcuXeTn56fw8HC9+uqrysnJf14dAAAAAIBr8fH00sjOfbizugtK3HFcCVuO6boeLVSzf3tVeugmnVm+T+c2HZEkWdzdVLlnS2WeSdaet2dq1xu/KeVgrAJvKK9rnV2jbIsauuG1zrrhtc4q26KGzm06IjdvD/lVLqcTv2xUlZ63KqprQ0VPW2u7QhgojSyyaHinFxTg7Wd2FMgFrsytW7euFi1aZHvu4fG/yAMGDNAff/yhX375RcHBwXrxxRfVvXt3rV69WpKUm5urLl26KDIyUmvWrNGpU6f0xBNPyNPTUyNHjnT4awEAAAAAFI/aEVXVp+WD+mjFD2ZHgR1Ozd2msNtrK6ThdZIk3/IhyopP1Zkle1WmaVVJkl/FMrp+YAflpmfJyLXKI8BHBz5eKL+KZfLdr0egj3JS8l65m5OcKY9An8uOz0nNVOz/tXfn8TXdif/H3zfbzb6QnSyohFiS2COlqiEtooyUokSVltHFWKqKVvmNZaoLOlUzY1DV6qqLajv40qI6LZpaG9vY2kS0SCwRWe7vD+NObxMkJE5u8no+HnnI/dzPOfd9NDlN3s79nDW7VX9kZ104+qvMAV7WD0uRRfknz8otxLdiDhqwMwNb3qM24U2MjoH/qvJlrpOTk4KDg0uM5+TkaNGiRXrzzTfVuXNnSdLixYvVuHFjffPNN2rXrp3+9a9/ac+ePVq7dq2CgoIUFxen6dOna8KECZo6dapcXFxKfc38/Hzl5+dbH+fm5kqSiouLVVxcXAlHWTYs6Y8bZfrdn0B5GHneA6qz4uJiWSwWvsdgl/i6RVWV1rqHNv0nXVuP7TE6CsqouKBIpt9dYmtyMMlisZSY6+h2+Xf4/JNnlXf89DXXtXWPqK1z+7MV0CHaOnZuf5bcI/xLnf/zx9/Lv0OUXHzdlXfslCxFvznPFRdLxSXzADVBg9p19XiH+42Ogd+o8mXu/v37FRoaKldXVyUkJGjmzJkKDw/Xtm3bVFBQoKSkJOvcRo0aKTw8XFu2bFG7du20ZcsWNWvWTEFB/1v0PDk5WSNHjtTu3bsVHx9f6mvOnDlTzz33XInxkydP6uLFq6/JU9lCnHwNe23YN5MkP0cPmSTxIwjKKzs72+gIQLVUXFysnJwcWSwWOThU6ZWvgBLOnj1rdASgVA4mB/35nj8qdekEnc0/b3QclIF341Bl/98eOfu5yzXIR3k/ndbJr/apVut61jlnfjgmJ0+znH3ddTEzRz9/vF3eTerIK/p/F34dfesbOfu4K6Rbc0mS/+1ROrjg/3Tyyx/l1ThUZ9KPKu/4adVNbVUiw9l9Wco/eVZh/S7f2MktrJbys88q98dMFZy5IJlMMgd6VfLfBFD1ODk4akb3R2V2Kv1iSBijSpe5bdu21ZIlSxQdHa3MzEw999xz6tChg3bt2qWsrCy5uLjI19fXZpugoCBlZWVJkrKysmyK3CvPX3nuaiZOnKgxY8ZYH+fm5iosLEwBAQHy9vauoKMrv8zCM4a9NuzblRI3q/AMZS7KLTCQmz0AlaG4uFgmk0kBAQGUubA7rq6lv00ZqAqCvf01KWmonvp0vtFRUAahvVroxBc79dMH21R4Ll/O3q6q3a6BApNirHMKz+Yp85PvVXju8jIJfi0jbZ6XpIIzF2yu8PWI9Ff4gARlfbFTWZ/tlIu/pyLSEuUa7GuzXXFBoX76cLsiBibI5HB5exdfd4X2itfxt7+VyclBYfe3lYNzla5PgErxx8T71Cgw0ugY+J0qfTa65557rJ83b95cbdu2VUREhN555x25ublV2uuazWaZzeYS4w4ODob+skUJh5tlEV9HKD9KJqDymEwmw3++AG4EX7Oo6u5pnKivDn2v1Xs3GR0F1+Ho6qzQe1so9N4WV53jf3uU/G+PuuZ+GozsXGLMNzZMvrFh19zOwdlJjZ7sVmK8dtsGqt22wTW3Baqz9pGxGtrmXqNjoBR29VOYr6+voqKidODAAQUHB+vSpUs6c+aMzZwTJ05Y19gNDg7WiRMnSjx/5TkAAAAAQPU0KWmoIvxCjI4BAHYnyKuWZnQbVWI9a1QNdlXmnjt3TgcPHlRISIhatmwpZ2dnrVu3zvp8RkaGjh49qoSEBElSQkKCdu7cabPe45o1a+Tt7a2YmJgS+wcAAAAAVA+eZne90HOMXJ1KvusSAFA6JwdH/aXHE/JzN26ZUVxblS5zx40bpy+//FKHDx/W119/rd69e8vR0VH9+/eXj4+PHnroIY0ZM0br16/Xtm3b9OCDDyohIUHt2rWTJHXt2lUxMTEaNGiQfvjhB33xxReaPHmyRo0aVeoyCgAAAACA6qNhQJimdB1mdAwAsBuPd+ivuDrRRsfANVTpNXOPHz+u/v3769dff1VAQIBuv/12ffPNNwoICJAkvfTSS3JwcFCfPn2Un5+v5ORkvfrqq9btHR0dtWrVKo0cOVIJCQny8PBQWlqapk2bZtQhAQAAAABuoR4xHZT+U4be/WGt0VEAoEq787bWSmvdw+gYuA6TxWLhfkjXkZubKx8fH+Xk5Mjb27jLzPsve9qw14Z9M0kKcfJVZuEZboCGcntr0AyjIwDVUnFxsbKzsxUYGMjNpGB3qsrPx0BZFRQVKu2tZ7U766DRUQCgSqrjE6gVg2bK29XD6Ci4Dn5zAAAAAABUa86OTpqTMlq+bl5GRwGAKsfF0VlzUkZT5NoJylwAAAAAQLUX6hOgGd0elQN3ZwcAG1O6DFNMcH2jY6CMKHMBAAAAADVCYr1YjUrsa3QMAKgyhrROUc+mdxgdA+VAmQsAAAAAqDGGteutHjEdjI4BAIbrWL+FnujY3+gYKCfKXAAAAABAjTI1+RHF14k2OgYAGOY2/zDN6vGYHExUg/aG/2IAAAAAgBrF2dFJL907VnV9Ao2OAgC3nJ+bl+b2Gi8PFzejo+AGUOYCAAAAAGocP3dvzev9pLzM7kZHAYBbxsnBUS/eO0Z1ffnHLHtFmQsAAAAAqJEa+NfVX1KekCNvMwZQQ0zq8pBa1G1sdAzcBP6PBQAAAACosdpHxmpC5yFGxwCASje8XW/9oVlno2PgJlHmAgAAAABqtH7xXTW4VXejYwBApenTvLMevb2f0TFQAShzAQAAAAA13pg7HlDPJncYHQMAKtydt7XWpKRhRsdABaHMBQAAAADUeCaTSVOTH9Gdt7UyOgoAVJgWdRtpdo/H5OhABVhd8F8SAAAAAABJjg4Omt3jcbXi5kAAqoGG/uGa22u8zE4uRkdBBaLMBQAAAADgv8xOLprbe7waBUYaHQUAbliot79eTX1K3q4eRkdBBaPMBQAAAADgNzzN7nq1z0SF+wUbHQUAys3PzVsLUp9WoGcto6OgElDmAgAAAADwO7U9fLQwdRJlCAC74ufmpb/3nazIWqFGR0ElocwFAAAAAKAUoT4B+nvfyQrw9DM6CgBcl6+blxbeN1kNA8KNjoJKRJkLAAAAAMBVRNYK1aJ+z3CFLoAqzcfVU6+lPq3owAijo6CSUeYCAAAAAHANEX4hWtTvGQV5UegCqHq8XT208L5JahxUz+gouAUocwEAAAAAuI5wv2AKXQBVjpfZQ6+lPk2RW4NQ5gIAAAAAUAZhvpcL3WCv2kZHAQB5md31WurTahLcwOgouIUocwEAAAAAKKMrhW6Il7/RUQDUYL5uXnotdZKahlDk1jSUuQAAAAAAlENd3yD9o98zCvMNMjoKgBooyKuWFt8/lSK3hqLMBQAAAACgnOr6Bmpp/+fUKDDS6CgAapAIvxAt7T9N9WvXMToKDEKZCwAAAADADajt4atF/Z5R67AmRkcBUAM0CozUkv7PKcSbZV5qMspcAAAAAABukKfZXa/2eUpJDdsYHQVANdaqbmMt6veMarl7Gx0FBqPMBQAAAADgJrg4Oev5nqOVGptkdBQA1VCnBi31aupEeZrdjY6CKoAyFwAAAACAm+RgctCULsP0SEIfo6MAqEbubdpJL9w7RmYnF6OjoIpwMjoAAAAAAADVxR8T75O/h49mrVuiIkux0XEA2CmTTHr09r4a1q630VFQxVDmAgAAAABQgfrGdVUdn0A9+clcnbuUZ3QcAHbGxdFZ0+4ZqXsatTc6CqogllkAAAAAAKCCJdaL09IB0xTqHWB0FAB2xM/NW3/rO5kiF1dFmQsAAAAAQCW4zT9Mbwz8f4oLjTI6CgA70NA/XMsf+H+KrxNtdBRUYZS5AAAAAABUktoePvpHv2d0b9NORkcBUIV1rN9CSwc8pzo+gUZHQRVHmQsAAAAAQCVydnTStLtHaFynQXI08Ws4gP8xyaShbe7V3N7j5OHiZnQc2AFugAYAAAAAwC0wqFV33eYfpomfztfpvLNGxwFgMC+zu6bf80fdeVsro6PAjvBPggAAAAAA3CIJkc319uBZig1taHQUAAaKDojQW4NmUOSi3ChzAQAAAAC4hYK8amtRv2f1QMtuRkcBYICeTTrq9QHTFeYbbHQU2CGWWQAAAAAA4BZzdnTS+DsHK75OI0394jWdzb9gdCQAlczF0VkTOqcpNTbJ6CiwY5S5AAAAAAAYJCmqjaIDwzX245eVkX3Y6DgAKkmot7/m9PyTmgQ3MDoK7BzLLAAAAAAAYKAw32AtGzBNfZp3NjoKgEpwd6P2envwbIpcVAiuzAUAAAAAwGBmJxc90/VhJUQ01/9bu0hn8s4aHQnATfJ0cdPEpKHqEdPB6CioRrgyFwAAAACAKqJLdDu9l/YXJUbGGh0FwE2IrxOtd9JmU+SiwlHmAgAAAABQhQR4+unV1ImalDRUrk5mo+MAKAcnB0c9ens/Ler3rOr4BBodB9UQyywAAAAAAFAF9Y3rqjbhTTVp9V+1K+ug0XEAXEe4X7BmdHtUzUJuMzoKqjGuzAUAAAAAoIqKrBWqpQOmaWT7VDk5OBodB0ApHEwm9Y+/W28PmkWRi0rHlbkAAAAAAFRhTg6OGtE+VR3rt9Bz//qbfsw+bHQkAP9Vv3ZdTU1+WLGhUUZHQQ3BlbkAAAAAANiBmOD6evOBGRpzx0DW0gUM5uzopBEJffTO4FkUubiluDIXAAAAAAA74ejgoLTWKUqKaqs/r1mkzYd/MDoSUOM0D2moZ5Mf1m3+YUZHQQ1EmQsAAAAAgJ2p4xOoV1MnavXeTXp+/TKdupBjdCSg2nNzNuuxDverf3yyHEy82R3GoMwFAAAAAMBOdWt8uxIj4/TCl2/oo10bjI4DVFtdo9ppTKcHFOLtb3QU1HCUuQAAAAAA2DEfN09Nu3uE/tDsTv3l/5Zq94lDRkcCqo2G/uGa0DlNrcObGB0FkESZCwAAAABAtRBXJ1rLH/izPtn9leZtWqGT504bHQmwWz6unhqV2FepsUlydGBJBVQdlLkAAAAAAFQTJpNJPZveoaSotvrntx/p9a2rlF9YYHQswG44mhyUGpukUYl95ePmaXQcoATKXAAAAAAAqhl3F1c9ens//aFZZ7301XL9K+MboyMBVV6rsBhN6JymqIAIo6MAV0WZCwAAAABANRXqE6DnU0arf/yPeunL5dqRud/oSECVExNUX4/e3k+J9WKNjgJcF2UuAAAAAADVXIu6jbRs4HR9eXCb/rr5XWVkHzY6EmC4erVCNSqxr5Ki2spkMhkdBygTVnAGAACoAC+88II6deqkkJAQmc1mRUREKC0tTYcOXb6j+NmzZzV69Gi1bNlS/v7+8vDwUGJiop555hmdPXvWZl8nTpzQ0KFDFRgYKLPZrJiYGL3yyitGHBYAoJq5o0FLvT1opp5PGa36teoYHQcwRIiXv6YmP6L3h8xRl+h2FLmwKyaLxWIxOkRVl5ubKx8fH+Xk5Mjb29uwHP2XPW3Ya8O+mSSFOPkqs/CM+IZHeb01aIbREQC7EBkZqaNHjyo6Olr5+fn6z3/+I0kKDg5WRkaGTp06pXr16slsNqtRo0b66aef9Msvv0iS7rnnHq1evVqSdP78ebVs2VIZGRlyc3NT3bp1tX//5bfETpkyRdOmTTPmAIHfqCo/HwO4OcWWYn26Z5Ne+/o9Hc/JNjoOUOn83Lw1vF0v3RfbRS5OzkbHAW4IV+YCAABUgOHDh+vw4cPau3evDh06pNGjR0uSsrKytG7dOrm6uur555/XyZMnlZ6eriNHjqhly5aSpM8++0ynT5+WJC1cuFAZGRkymUz65ptvtG/fPo0ZM0aSNGvWLJ04ccKQ4wMAVD8OJgelNOmojx56SVO6DFOot7/RkYBKEexVW0/eOVifPTxfA1t2o8iFXavSZe7MmTPVunVreXl5KTAwUL169VJGRobNnE6dOslkMtl8jBgxwmbO0aNH1b17d7m7uyswMFDjx49XYWHhrTwUAABQzU2aNEnh4eHWxx06dLB+bjabFRwcrHHjxsnLy0uS5Orqqri4OEmSg4ODnJwu38rgs88+kyQ1bNhQzZs3lyT16dNHklRQUKB169ZV+rEAAGoWJwdHpcYm6ZNhczWj26OKCgi//kaAHahXK1TPJY/QqmFzNbBlN7k5m42OBNy0Kn0DtC+//FKjRo1S69atVVhYqKefflpdu3bVnj175OHhYZ03fPhwm7ccuru7Wz8vKipS9+7dFRwcrK+//lqZmZkaPHiwnJ2dNWMGbx0GAAAVr6ioSH/7298kSfXr19ddd91VYk52drY+/fRTSdL9999vLXmPHTsmSQoMDLTODQoKsn5+9OjRSssNAKjZnBwc1T3mdnWPuV2b/5Ouxd9+ou+O7TY6FlBuTYLqa2jbXurcsJUcTFX6Okag3Kp0mfv555/bPF6yZIkCAwO1bds2dezY0Tru7u6u4ODgUvfxr3/9S3v27NHatWsVFBSkuLg4TZ8+XRMmTNDUqVPl4uJSYpv8/Hzl5+dbH+fm5kqSiouLVVxcXBGHdkNYjhs3yvS7P4HyMPK8B9ij8+fPa8CAAfriiy8UHBysjz76SM7OzjbfSwcPHlT37t2VlZWl9u3b69VXXy31e+3KWFFRkXXMYrHwfQnD8TUIVH+J9eKUWC9OuzIPasl3H2vd/m9VzC13UMW1CW+ih9r2UruIZkZHASpNlS5zfy8nJ0eSVKtWLZvx5cuX64033lBwcLBSUlI0ZcoU69W5W7ZsUbNmzWyuaElOTtbIkSO1e/duxcfHl3idmTNn6rnnnisxfvLkSV28eLEiD6lcQpx8DXtt2DeTJD9HD5kkboCGcsvO5mYYQFllZ2dr0KBB2rFjhxo0aKDly5fL39/f5vto69atSktL06lTp3TnnXfq73//u/Ly8pSXlyfp8lW4GRkZyszMtG7322WmfHx8+L6E4c6ePWt0BAC3SNOQBprT8086ejpLb2x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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSIGHTS:\n",
      "==============================\n",
      "• The dataset has 11.0% anomaly rate\n",
      "• This is a reasonable balance for anomaly detection\n",
      "• Sufficient samples for both normal and anomaly classes\n"
     ]
    }
   ],
   "source": [
    "# Visualize class distribution\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Bar plot\n",
    "colors = ['#2E8B57', '#DC143C']  # Green for normal, Red for anomaly\n",
    "class_labels = ['Normal (1)', 'Anomaly (2-5)']\n",
    "class_values = [class_counts[1], class_counts.sum() - class_counts[1]]\n",
    "\n",
    "bars = ax1.bar(class_labels, class_values, color=colors, alpha=0.8)\n",
    "ax1.set_title('Class Distribution', fontsize=14, fontweight='bold')\n",
    "ax1.set_ylabel('Number of Samples')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Add value labels on bars\n",
    "for bar, value in zip(bars, class_values):\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 10, \n",
    "             f'{value:,}', ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "# Pie chart\n",
    "ax2.pie(class_values, labels=class_labels, colors=colors, autopct='%1.1f%%', \n",
    "        startangle=90, explode=(0, 0.1))\n",
    "ax2.set_title('Class Distribution (%)', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"INSIGHTS:\")\n",
    "print(\"=\" * 30)\n",
    "print(f\"• The dataset has {anomaly_rate:.1%} anomaly rate\")\n",
    "print(f\"• This is a reasonable balance for anomaly detection\")\n",
    "print(f\"• Sufficient samples for both normal and anomaly classes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Time Series Feature Analysis\n",
    "\n",
    "### Purpose:\n",
    "- Analyze time series characteristics\n",
    "- Examine statistical properties of each feature\n",
    "- Identify distribution patterns\n",
    "- Understand feature ranges and variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME SERIES FEATURE ANALYSIS:\n",
      "==================================================\n",
      "Feature Statistics Summary:\n",
      "  Mean range: [-3.740, 1.474]\n",
      "  Std range: [0.179, 1.504]\n",
      "  Min range: [-6.281, -0.936]\n",
      "  Max range: [0.917, 7.402]\n",
      "\n",
      "Features with zero variance: 0\n",
      "All features have non-zero variance\n"
     ]
    }
   ],
   "source": [
    "# Analyze time series features\n",
    "print(\"TIME SERIES FEATURE ANALYSIS:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Calculate basic statistics for each feature\n",
    "feature_stats = df.drop('target', axis=1).describe()\n",
    "\n",
    "print(\"Feature Statistics Summary:\")\n",
    "print(f\"  Mean range: [{feature_stats.loc['mean'].min():.3f}, {feature_stats.loc['mean'].max():.3f}]\")\n",
    "print(f\"  Std range: [{feature_stats.loc['std'].min():.3f}, {feature_stats.loc['std'].max():.3f}]\")\n",
    "print(f\"  Min range: [{feature_stats.loc['min'].min():.3f}, {feature_stats.loc['min'].max():.3f}]\")\n",
    "print(f\"  Max range: [{feature_stats.loc['max'].min():.3f}, {feature_stats.loc['max'].max():.3f}]\")\n",
    "\n",
    "# Check for features with zero variance\n",
    "zero_var_features = feature_stats.loc['std'][feature_stats.loc['std'] == 0]\n",
    "print(f\"\\nFeatures with zero variance: {len(zero_var_features)}\")\n",
    "if len(zero_var_features) > 0:\n",
    "    print(\"Warning: These features should be removed as they provide no information\")\n",
    "else:\n",
    "    print(\"All features have non-zero variance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEATURE DISTRIBUTION ANALYSIS:\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSIGHTS:\n",
      "==============================\n",
      "• Analyzed 12 out of 140 features\n",
      "• Features show varying distributions and ranges\n",
      "• Some features appear normally distributed, others are skewed\n"
     ]
    }
   ],
   "source": [
    "# Visualize feature distributions\n",
    "print(\"FEATURE DISTRIBUTION ANALYSIS:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Select a subset of features for visualization\n",
    "n_features_to_plot = min(12, len(feature_names))\n",
    "selected_features = feature_names[:n_features_to_plot]\n",
    "\n",
    "fig, axes = plt.subplots(3, 4, figsize=(16, 12))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i, feature in enumerate(selected_features):\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Plot histogram\n",
    "    ax.hist(df[feature], bins=30, alpha=0.7, color='skyblue', edgecolor='black')\n",
    "    ax.set_title(f'Feature {feature}', fontsize=10)\n",
    "    ax.set_xlabel('Value')\n",
    "    ax.set_ylabel('Frequency')\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add statistics text\n",
    "    mean_val = df[feature].mean()\n",
    "    std_val = df[feature].std()\n",
    "    ax.text(0.02, 0.98, f'μ={mean_val:.2f}\\nσ={std_val:.2f}', \n",
    "            transform=ax.transAxes, verticalalignment='top',\n",
    "            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "# Remove extra subplots\n",
    "for i in range(n_features_to_plot, len(axes)):\n",
    "    fig.delaxes(axes[i])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Feature Distributions (First 12 Features)', fontsize=16, fontweight='bold', y=1.02)\n",
    "plt.show()\n",
    "\n",
    "print(f\"INSIGHTS:\")\n",
    "print(\"=\" * 30)\n",
    "print(f\"• Analyzed {n_features_to_plot} out of {len(feature_names)} features\")\n",
    "print(f\"• Features show varying distributions and ranges\")\n",
    "print(f\"• Some features appear normally distributed, others are skewed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Time Series Visualization\n",
    "\n",
    "### Purpose:\n",
    "- Visualize sample time series from different classes\n",
    "- Identify pattern differences between normal and anomaly\n",
    "- Understand time series characteristics and trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME SERIES VISUALIZATION:\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INSIGHTS:\n",
      "==============================\n",
      "• Normal samples show more consistent patterns\n",
      "• Anomaly samples show more variability and unusual patterns\n",
      "• Time series length is 140 time steps\n",
      "• Patterns suggest potential for anomaly detection\n"
     ]
    }
   ],
   "source": [
    "# Visualize sample time series\n",
    "print(\"TIME SERIES VISUALIZATION:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Get sample indices\n",
    "normal_indices = np.where(df['target'] == 1)[0][:3]\n",
    "anomaly_indices = np.where(df['target'] != 1)[0][:3]\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "# Plot normal samples\n",
    "for i, idx in enumerate(normal_indices):\n",
    "    axes[0, i].plot(X.iloc[idx], color='#2E8B57', linewidth=1.5)\n",
    "    axes[0, i].set_title(f'Normal Sample {i+1}', fontweight='bold')\n",
    "    axes[0, i].set_xlabel('Time Step')\n",
    "    axes[0, i].set_ylabel('Amplitude')\n",
    "    axes[0, i].grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add statistics\n",
    "    mean_val = np.mean(X.iloc[idx])\n",
    "    std_val = np.std(X.iloc[idx])\n",
    "    axes[0, i].text(0.02, 0.98, f'μ={mean_val:.2f}\\nσ={std_val:.2f}', \n",
    "                    transform=axes[0, i].transAxes, verticalalignment='top',\n",
    "                    bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "# Plot anomaly samples\n",
    "for i, idx in enumerate(anomaly_indices):\n",
    "    axes[1, i].plot(X.iloc[idx], color='#DC143C', linewidth=1.5)\n",
    "    axes[1, i].set_title(f'Anomaly Sample {i+1}', fontweight='bold')\n",
    "    axes[1, i].set_xlabel('Time Step')\n",
    "    axes[1, i].set_ylabel('Amplitude')\n",
    "    axes[1, i].grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add statistics\n",
    "    mean_val = np.mean(X.iloc[idx])\n",
    "    std_val = np.std(X.iloc[idx])\n",
    "    axes[1, i].text(0.02, 0.98, f'μ={mean_val:.2f}\\nσ={std_val:.2f}', \n",
    "                    transform=axes[1, i].transAxes, verticalalignment='top',\n",
    "                    bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Sample Time Series: Normal vs Anomaly', fontsize=16, fontweight='bold', y=1.02)\n",
    "plt.show()\n",
    "\n",
    "print(\"INSIGHTS:\")\n",
    "print(\"=\" * 30)\n",
    "print(\"• Normal samples show more consistent patterns\")\n",
    "print(\"• Anomaly samples show more variability and unusual patterns\")\n",
    "print(\"• Time series length is 140 time steps\")\n",
    "print(\"• Patterns suggest potential for anomaly detection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Statistical Feature Engineering\n",
    "\n",
    "### Purpose:\n",
    "- Create statistical features for anomaly detection\n",
    "- Extract time series characteristics\n",
    "- Generate features that can help identify anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEATURE ENGINEERING FOR ANOMALY DETECTION:\n",
      "============================================================\n",
      "Extracted 12 statistical features\n",
      "Feature names: ['mean', 'std', 'median', 'mad', 'range', 'iqr', 'cv', 'skewness', 'kurtosis', 'peak_count', 'zero_crossings', 'trend']\n",
      "Feature shape: (2000, 13)\n",
      "\n",
      "ENGINEERED FEATURE STATISTICS:\n",
      "         mean       std    median       mad     range       iqr            cv  \\\n",
      "count  2000.0  2000.000  2000.000  2000.000  2000.000  2000.000  2.000000e+03   \n",
      "mean     -0.0     0.996     0.095     0.420     6.185     0.855  9.213711e+07   \n",
      "std       0.0     0.000     0.112     0.105     0.627     0.212  5.161722e+06   \n",
      "min      -0.0     0.996    -0.319     0.087     2.638     0.158  7.226435e+07   \n",
      "25%      -0.0     0.996     0.023     0.364     5.980     0.747  8.894783e+07   \n",
      "50%       0.0     0.996     0.073     0.434     6.288     0.867  9.285084e+07   \n",
      "75%       0.0     0.996     0.143     0.484     6.539     0.976  9.621955e+07   \n",
      "max       0.0     0.996     0.644     0.888    11.218     2.037  9.964222e+07   \n",
      "\n",
      "       skewness  kurtosis  peak_count  zero_crossings     trend    target  \n",
      "count  2000.000  2000.000    2000.000        2000.000  2000.000  2000.000  \n",
      "mean     -1.562     5.094      18.420           6.983     0.012     1.137  \n",
      "std       0.557     2.042       4.107           3.891     0.007     0.450  \n",
      "min      -4.241    -1.677       5.000           1.000    -0.024     1.000  \n",
      "25%      -1.914     3.861      16.000           4.000     0.011     1.000  \n",
      "50%      -1.576     5.039      18.000           6.000     0.014     1.000  \n",
      "75%      -1.265     6.273      20.000           9.000     0.016     1.000  \n",
      "max       2.487    21.365      36.000          53.000     0.024     5.000  \n"
     ]
    }
   ],
   "source": [
    "# Create statistical features for anomaly detection\n",
    "print(\"FEATURE ENGINEERING FOR ANOMALY DETECTION:\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "def extract_anomaly_features(X):\n",
    "    \"\"\"Extract features useful for unsupervised anomaly detection\"\"\"\n",
    "    features = {}\n",
    "    \n",
    "    # Convert to numpy array for easier processing\n",
    "    X_array = X.values\n",
    "    \n",
    "    # Basic statistical features\n",
    "    features['mean'] = np.mean(X_array, axis=1)\n",
    "    features['std'] = np.std(X_array, axis=1)\n",
    "    features['median'] = np.median(X_array, axis=1)\n",
    "    features['mad'] = np.median(np.abs(X_array - np.median(X_array, axis=1, keepdims=True)), axis=1)  # MAD\n",
    "    \n",
    "    # Range and variability\n",
    "    features['range'] = np.max(X_array, axis=1) - np.min(X_array, axis=1)\n",
    "    features['iqr'] = np.subtract(*np.percentile(X_array, [75, 25], axis=1))\n",
    "    features['cv'] = features['std'] / (np.abs(features['mean']) + 1e-8)  # Coefficient of variation\n",
    "    \n",
    "    # Shape features\n",
    "    features['skewness'] = stats.skew(X_array, axis=1)\n",
    "    features['kurtosis'] = stats.kurtosis(X_array, axis=1)\n",
    "    \n",
    "    # Peak analysis\n",
    "    peak_counts = []\n",
    "    for i in range(X_array.shape[0]):\n",
    "        peaks, _ = find_peaks(X_array[i, :], height=np.mean(X_array[i, :]))\n",
    "        peak_counts.append(len(peaks))\n",
    "    features['peak_count'] = peak_counts\n",
    "    \n",
    "    # Zero crossing rate\n",
    "    zero_crossings = []\n",
    "    for i in range(X_array.shape[0]):\n",
    "        crossings = np.sum(np.diff(np.sign(X_array[i, :])) != 0)\n",
    "        zero_crossings.append(crossings)\n",
    "    features['zero_crossings'] = zero_crossings\n",
    "    \n",
    "    # Trend features\n",
    "    features['trend'] = np.polyfit(np.arange(X_array.shape[1]), X_array.T, deg=1)[0]  # Linear trend\n",
    "    \n",
    "    return pd.DataFrame(features)\n",
    "\n",
    "# Extract features\n",
    "anomaly_features = extract_anomaly_features(X)\n",
    "anomaly_features['target'] = y  # Add labels for analysis\n",
    "\n",
    "print(f\"Extracted {len(anomaly_features.columns)-1} statistical features\")\n",
    "print(f\"Feature names: {list(anomaly_features.columns[:-1])}\")\n",
    "print(f\"Feature shape: {anomaly_features.shape}\")\n",
    "\n",
    "# Show feature statistics\n",
    "print(\"\\nENGINEERED FEATURE STATISTICS:\")\n",
    "print(anomaly_features.describe().round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENGINEERED FEATURE ANALYSIS BY CLASS:\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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JtWvXVu3atR1atytkZmYqJCRErVq1cvu2S+KpMQEAoKI6ffq0evfurWPHjkmSatWqpUcffVStWrXS2bNntWnTJi1YsECS1KRJE7Vt21bJyclKT0/XmjVrdNttt9nWlZeXp+XLl9veDxo0qNRxjBgxQiNGjNCJEyf06aef6r333pNhGHr77bcVEhJid9Z6x44dy9hrx1mtVuXk5KhatWoe2X5JvDEmAABcZfbs2UpJSZEkde3aVYmJiapataqOHDmi7du3a8WKFSWuY+3atRo0aJDy8vIkSW+++aYGDx5caNsRI0bo3nvvtZtWtWpV28/t27fXqlWrZBiGtm7dqj59+khSgTtTbd261fYYlC1bttimd+jQocg4S3sM61JLly5VTEyM3bT69evbfn722Wd17tw5SdIdd9yhYcOGKSAgQIcPH9Y333yj3bt329q+//772rt3ryTp6quv1mOPPaaaNWvq2LFj2rlzp/773/+WOq585Hll440xAeXKs3VHAI4q6oyrjz/+2Da9cePGBZazWq1G7dq1DUlGcHCw0a9fv2LPhLn8bClJxrvvvmubf+n0/LOlTp8+bYSGhtqmjxs3rtA+5OTkGHv37rW9v/SKvzp16jg1Lpdf8Xep9PR0uzPwhw4dapt36XguWLDANj05Odm47bbbjMjISCMgIMCIiIgw2rRpY/zjH/8wjh49WuIVhvljemm/du3aZXTr1s0ICQmxnTXVqVMnW5vDhw8X6EunTp2MnTt3Gp07dzYqV65sxMbGGhMnTjRyc3NtsS5YsMDWftKkSYWOSf72ijtbLL9NUWNiGIaRlJRk/O1vfzOio6ONSpUqGdHR0UZCQoKxY8cOu3aXx/Sf//zHaNGihREYGGg0atTI+PDDDx37gAEA8GITJkywfe9Vq1bNOH78eIE2aWlptrPZn3/+eVv7IUOG2LVbvXq1bV6rVq1K3Pal3+2X5gGGYRizZ8+2zfPz8zP2799vm1dY7nXhwgXjmWeeMVq0aGEEBwcbQUFBRlxcnNGnTx/j7bffLrC90uQS8+fPN6ZNm2bUrl3b8PPzMzZs2GCX+12az1667rVr1xqTJ082rrzySiM4ONi48cYbjaSkJLv+XZqvFjUmGzZscCpvu1R2drbx3HPPGW3atDGqVKliVK5c2WjdurUxY8YMIzs7u8iYUlJSjKFDhxrh4eFG1apVjTvuuMPuigYAANypV69etu+oXbt2FZifmZlp9/7y409ff/21UaVKlUKvhstXXF5yuenTp9vaXnqHg8aNGxuSjBYtWhiSjH79+tnm9e/f37bMp59+apvuzDGsy6fnH5MpStOmTW1tMzIyCsy/dPweeOABW9uVK1cWaJuTk2N3XKco5HnkeYCzeMYfYBKGYdh+rlWrVoH53333ne0s9N69e2vEiBG2eYVdCXipa665RpI0Y8YM2/3TC7Nq1SqdPXtWkhQeHq5nnnmm0HaVKlVSs2bNit2mK4WFhdndq37ZsmUF7vd+qVOnTql79+5auXKlTp48qby8PJ0+fVo//vij3nzzTf3yyy8Ox3DmzBndfPPNWrduXanu457vyJEj6tSpkzZu3Ki//vpLKSkpeuaZZzRq1CiHYyirlStXqkOHDvrvf/+rtLQ05ebmKi0tTR9//LHi4+Nt9/S/3H/+8x8NGzZMe/bsUU5Ojg4cOKDBgwdr//79bu4BAADl48MPP7T9/PDDD+uKK64o0CYqKkoRERGSLl7FZ7FYJF38fs3Ozra1++ijj2w/O3K1X2EeeughNWnSRNLFs7AvXXdhpk+frokTJ2rPnj3KyspSdna2/ve//+nzzz+3XbHoqOnTp+upp57SsWPHis0jLzd+/HhNnjxZx48fV1ZWlr7++mvdfPPNTuVhZZGdna3u3bvriSee0I8//qjz58/rr7/+0q5duzRhwgR17969yLzyhhtu0Pvvv68zZ87o3Llz+uijj/TPf/7TrfEDAJAvNDTU9vPEiRP1zTff2H2HValSpchl9+7dq1tuuUXnz5+XJE2dOlVjx44tUzzx8fG2n/Ov5Dt9+rTtuz5//ZdeAXjpz0Vd8VfaY1iOunT8xo4dqx07dtiufJTsx+/Sts8//7zWrl1rGzvp4nExR65GLAx5XtmR58HMKPwBFdiJEyf0zTffaMWKFZo2bZpt+j/+8Y8CbS8t7v3tb39Tz549bYnIxx9/bJesXG7ChAmyWCz6+eefi70dwY8//mj7OT4+XpUrV7a937Ztm7755hu7V2GOHj1a4GHKdevWLXKbpXVpQnn+/Plik4ktW7bo5MmTkqTBgwdr7dq1WrFihV588UV16tRJ/v7+io2N1ddff63evXvblnvttdf09ddf6+uvv9ZVV11lt8709HT5+/vr3//+t9asWaO///3vpYr76NGj6tChgz799FNNmzZN/v7+ki7eTmPXrl2l7n++f/3rX3a3Um3btq0t5tmzZxe5XGZmpkaOHKnc3FxJ0oMPPqjPP/9cDz30kCQpNzdXI0eOLLSo+euvv2rkyJFatWqVunbtKuliUvr22287HD8AAN7mDozKggAA3bpJREFU3Llz+vXXX23vb7zxxhKXqV27ti03OXv2rL744gtJF2/zeelttspa+PPz89N1111ne5+cnFxs+08++UTSxRO43n//fa1bt07vvfeeHnjgAcXGxkpyPJf49ddfNWTIEH322Wd67733Ci2KFubgwYN69dVXtWLFCtsBvIyMDE2YMKFUy1/K0bztUq+88oo2b94sSYqLi9PixYv1wQcf2G6LvnnzZs2aNavQZf/66y+9//77mjt3rgIDAyVJS5YsUXp6usN9AACgrLp162b7eeXKlbrxxhsVGhqqjh076qWXXir2JOXvv//e9v316KOP6qmnnipxe1OmTClwfOfSR8Nce+21tmMc27dvl9VqtRX24uLilJCQIIvForS0NB0+fFhHjhxRamqqJKlBgwaqWbNmodst7TGsy9WrV69AvEeOHLHNv3T83nnnHV177bWqVq2aunfvrrfeest2vOTytt9++6169OihsLAwXXPNNZoyZYr++OOPUsdVFPK8i8jzgMLxjD+gAvviiy9sB4qki2eSv/DCCwUOEl24cMGW7AQFBenWW29VcHCw+vbtqyVLlujUqVNau3at3ZfkpZo3b66BAwfq448/1vTp03X77bcX2u7SL7fw8HC7eX379tWpU6fspl16lWJ5y09i8hX3RVypUiXbz3FxcWrSpImuvPJKWSwWjR8/3javY8eOioqKsr1v1apVsfcMf//999W9e3eH4q5SpYo++ugjVatWTbfccot+/vlnLVq0SNLFpK1169YOra9Ro0Z2/Svtvde//PJLW2Larl07zZ07V9LFq0e3bdumpKQk/fHHH1q7dq369+9vt2ybNm1sRb6aNWtq/fr1ki4megAAVHSX5xSF3XmhMIMHD9Z3330n6eJVfv3799e6det0+vRpSdJ1111n91wZZ12aA5V0ICI/RwgJCVGDBg3UunVrValSRcOGDbO1cTSXyD8b+lK//fZbiXE//PDDtrOmmzdvrsaNG0uSPv/8c+Xm5trFUJKgoCCH87Z8ixcvtv08d+5c3XLLLZIuPqPo1ltvlXTxBLvHH3+8wLJz58615UUrV67U6tWrdeHCBR05ckRt2rQpdfwAALjCyJEjtXnzZtsxBUnKycnRt99+q2+//VZvvPGGvv/+e1WvXr3IdVgsFoePaxSlatWqatmypX788UedPXtWu3fvthX+4uPjVb16dTVp0kQ///xzgef+Ffd8v9Iew3LUhAkTtGPHDq1du9Y27fz581q3bp3WrVun+fPn6+uvv1alSpXUo0cPPf7445o5c6bt2NeFCxeUlJSkpKQkzZkzR/+PvfuOj6La/z/+3vSeSEhDqvTQBcSASjVBEEFQUZBiAUWqICD3IlVFEAsqgt6LYAELXlDhIghKUYo06VyaNA1JqKmk7v7+4Jf5ZkmBJJtssryej8c+YGfOzJw5k9k9ez5zztm6datq1qxZrDxRz6OeB+SHHn+AAzl//rwOHjyYa/kvv/yi2NhYSVJkZKTR0y9n5edGw31OnDhRkrRv3758h3T09/c3/v/XX38VLvP/X2hoqPFUTvarKJMeX+/6ikfOvF7v3nvvVe3atSVJs2bNUtWqVeXv76927drpX//6V5GGivDw8ChS5bhevXpWec35NFfO3gUlLWcPyVatWlmty5mnvHpStm3b1vh/YGCg8f8rV67YMIcAANjH9XWK6Ojom9ru0UcfNZ5yX7FihVJTU62esC5ub79sOetABdV/pGsNgtnbREREyMfHR7Vq1dJzzz1X5KGXshtQCitnfaN27dpGI2RqaupNl7Et5FcHulH9R6IOBAAoW5ydnfXFF19o27ZtGjNmjJo1ayYnp/9rGj5x4oTefPPNPLfNHqLcYrHo4YcfNh5eKshTTz2Vq33n6aeftkqTM4C3detWI8CXvTx7hIRt27bd1DCf2W6mDet6S5cuzZXfnIE1X19frVmzRuvWrdOQIUNyTWHz+++/Ww2Z+cYbb2jfvn165ZVX1KpVK6uhPc+fP39TvSZvhHpe8VDPgyMj8AeUYwMGDFBGRoZWr14tLy8vWSwWzZo1SytWrLBK99VXXxn/X7FihTFkQa9evYzl33//vVJTU/M9VtOmTY0v9Ndeey3PNDmfaNm/f78x358kXbhwQVevXr3hOWU/qZPzld3tvzg2b95s/N/T09N4migvXl5e2rx5s6ZNm6YOHTooNDRUiYmJ2rhxowYPHqxZs2YV+vg5nzwqjuzKdn7LsrKyjP/bYuiIouYpp5xPC+as6JZmj08AAEqKj4+PVc+8nHWOgoSEhKh9+/aSrg0X+v333xvDfDo5Oemxxx4rdt6ysrL0+++/G++bNm1aYPpnn31WP/74o/r166eGDRvKzc1NJ06c0Mcff6y2bdsWqSEjJCSk0NvkpazVgW5U/5GoAwEAyqZWrVpp9uz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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KEY INSIGHTS:\n",
      "========================================\n",
      "• MAD (Median Absolute Deviation) shows good separation between classes\n",
      "• Peak count differs between normal and anomaly patterns\n",
      "• Zero crossings indicate different signal complexity\n",
      "• Range and CV show variability differences\n",
      "• Skewness indicates distribution shape differences\n"
     ]
    }
   ],
   "source": [
    "# Visualize engineered features by class\n",
    "print(\"ENGINEERED FEATURE ANALYSIS BY CLASS:\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Select key features for visualization\n",
    "key_features = ['mad', 'peak_count', 'zero_crossings', 'range', 'cv', 'skewness']\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i, feature in enumerate(key_features):\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Plot distributions by class\n",
    "    normal_data = anomaly_features[anomaly_features['target'] == 1][feature]\n",
    "    anomaly_data = anomaly_features[anomaly_features['target'] != 1][feature]\n",
    "    \n",
    "    ax.hist(normal_data, bins=30, alpha=0.7, label='Normal', color='#2E8B57')\n",
    "    ax.hist(anomaly_data, bins=30, alpha=0.7, label='Anomaly', color='#DC143C')\n",
    "    \n",
    "    ax.set_title(f'{feature.upper()} Distribution', fontweight='bold')\n",
    "    ax.set_xlabel(feature.upper())\n",
    "    ax.set_ylabel('Frequency')\n",
    "    ax.legend()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add statistics\n",
    "    normal_mean = normal_data.mean()\n",
    "    anomaly_mean = anomaly_data.mean()\n",
    "    ax.text(0.02, 0.98, f'Normal μ={normal_mean:.2f}\\nAnomaly μ={anomaly_mean:.2f}', \n",
    "            transform=ax.transAxes, verticalalignment='top',\n",
    "            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Engineered Features: Normal vs Anomaly', fontsize=16, fontweight='bold', y=1.02)\n",
    "plt.show()\n",
    "\n",
    "print(\"KEY INSIGHTS:\")\n",
    "print(\"=\" * 40)\n",
    "print(\"• MAD (Median Absolute Deviation) shows good separation between classes\")\n",
    "print(\"• Peak count differs between normal and anomaly patterns\")\n",
    "print(\"• Zero crossings indicate different signal complexity\")\n",
    "print(\"• Range and CV show variability differences\")\n",
    "print(\"• Skewness indicates distribution shape differences\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Correlation Analysis\n",
    "\n",
    "### Purpose:\n",
    "- Analyze feature correlations\n",
    "- Identify multicollinearity\n",
    "- Understand feature importance for anomaly detection\n",
    "- Highlight significant correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORRELATION ANALYSIS:\n",
      "========================================\n",
      "Moderately to highly correlated feature pairs (|r| > 0.5): 7\n",
      "\n",
      "Significant correlation pairs:\n",
      "Feature 1\t\tFeature 2\t\tCorrelation\tP-value\tSignificance\n",
      "--------------------------------------------------------------------------------\n",
      "mad         \tiqr         \t   0.907\t   0.000\t***\n",
      "skewness    \tkurtosis    \t  -0.751\t   0.000\t***\n",
      "median      \tpeak_count  \t   0.667\t   0.000\t***\n",
      "mad         \ttrend       \t   0.568\t   0.000\t***\n",
      "range       \tkurtosis    \t   0.555\t   0.000\t***\n",
      "median      \ttrend       \t  -0.531\t   0.000\t***\n",
      "iqr         \tskewness    \t   0.502\t   0.000\t***\n"
     ]
    }
   ],
   "source": [
    "# Correlation analysis with statistical significance\n",
    "print(\"CORRELATION ANALYSIS:\")\n",
    "print(\"=\" * 40)\n",
    "\n",
    "# Calculate correlation matrix for engineered features\n",
    "feature_data = anomaly_features.drop('target', axis=1)\n",
    "feature_corr = feature_data.corr()\n",
    "\n",
    "# Calculate p-values for statistical significance\n",
    "n_samples = len(feature_data)\n",
    "p_values = np.zeros_like(feature_corr)\n",
    "\n",
    "for i in range(len(feature_corr.columns)):\n",
    "    for j in range(len(feature_corr.columns)):\n",
    "        if i != j:\n",
    "            corr_val, p_val = pearsonr(feature_data.iloc[:, i], feature_data.iloc[:, j])\n",
    "            p_values[i, j] = p_val\n",
    "        else:\n",
    "            p_values[i, j] = 0.0  # Perfect correlation with self\n",
    "\n",
    "# Find highly correlated features\n",
    "high_corr_pairs = []\n",
    "for i in range(len(feature_corr.columns)):\n",
    "    for j in range(i+1, len(feature_corr.columns)):\n",
    "        corr_val = feature_corr.iloc[i, j]\n",
    "        p_val = p_values[i, j]\n",
    "        if abs(corr_val) > 0.5:  # Moderate to high correlation\n",
    "            significance = \"***\" if p_val < 0.001 else \"**\" if p_val < 0.01 else \"*\" if p_val < 0.05 else \"\"\n",
    "            high_corr_pairs.append((feature_corr.columns[i], feature_corr.columns[j], corr_val, p_val, significance))\n",
    "\n",
    "print(f\"Moderately to highly correlated feature pairs (|r| > 0.5): {len(high_corr_pairs)}\")\n",
    "if high_corr_pairs:\n",
    "    print(\"\\nSignificant correlation pairs:\")\n",
    "    print(\"Feature 1\\t\\tFeature 2\\t\\tCorrelation\\tP-value\\tSignificance\")\n",
    "    print(\"-\" * 80)\n",
    "    for feat1, feat2, corr, p_val, sig in sorted(high_corr_pairs, key=lambda x: abs(x[2]), reverse=True):\n",
    "        print(f\"{feat1:12}\\t{feat2:12}\\t{corr:8.3f}\\t{p_val:8.3f}\\t{sig}\")\n",
    "else:\n",
    "    print(\"No highly correlated features found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORRELATION INSIGHTS:\n",
      "========================================\n",
      "• Most features show moderate correlations\n",
      "• Some features are highly correlated (potential redundancy)\n",
      "• Feature selection may be beneficial for modeling\n",
      "• Statistical significance indicated by asterisks (* p<0.05, ** p<0.01, *** p<0.001)\n",
      "\n",
      "FEATURE DESCRIPTIONS:\n",
      "• mean: Mean value\n",
      "• std: Standard deviation\n",
      "• median: Median value\n",
      "• mad: Median Absolute Deviation\n",
      "• range: Range (max-min)\n",
      "• iqr: Interquartile Range\n",
      "• cv: Coefficient of Variation\n",
      "• skewness: Distribution skewness\n",
      "• kurtosis: Distribution kurtosis\n",
      "• peak_count: Number of peaks\n",
      "• zero_crossings: Zero crossing rate\n",
      "• trend: Linear trend coefficient\n"
     ]
    }
   ],
   "source": [
    "# Create improved correlation matrix visualization\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Create mask for upper triangle\n",
    "mask = np.triu(np.ones_like(feature_corr, dtype=bool))\n",
    "\n",
    "# Use built-in colormap instead of custom one\n",
    "cmap = 'RdBu_r'  # Red-Blue diverging colormap\n",
    "\n",
    "# Plot correlation matrix\n",
    "sns.heatmap(feature_corr, \n",
    "            mask=mask, \n",
    "            annot=True, \n",
    "            cmap=cmap, \n",
    "            center=0,\n",
    "            square=True, \n",
    "            linewidths=0.5, \n",
    "            cbar_kws={\"shrink\": .8, \"label\": \"Pearson Correlation Coefficient\"},\n",
    "            fmt='.2f',\n",
    "            annot_kws={'size': 9})\n",
    "\n",
    "plt.title('Feature Correlation Matrix\\n(Upper triangle masked for clarity)', \n",
    "          fontsize=16, fontweight='bold', pad=20)\n",
    "plt.xlabel('Features', fontsize=12)\n",
    "plt.ylabel('Features', fontsize=12)\n",
    "\n",
    "# Add feature descriptions\n",
    "feature_descriptions = {\n",
    "    'mean': 'Mean value',\n",
    "    'std': 'Standard deviation',\n",
    "    'median': 'Median value',\n",
    "    'mad': 'Median Absolute Deviation',\n",
    "    'range': 'Range (max-min)',\n",
    "    'iqr': 'Interquartile Range',\n",
    "    'cv': 'Coefficient of Variation',\n",
    "    'skewness': 'Distribution skewness',\n",
    "    'kurtosis': 'Distribution kurtosis',\n",
    "    'peak_count': 'Number of peaks',\n",
    "    'zero_crossings': 'Zero crossing rate',\n",
    "    'trend': 'Linear trend coefficient'\n",
    "}\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"CORRELATION INSIGHTS:\")\n",
    "print(\"=\" * 40)\n",
    "print(\"• Most features show moderate correlations\")\n",
    "print(\"• Some features are highly correlated (potential redundancy)\")\n",
    "print(\"• Feature selection may be beneficial for modeling\")\n",
    "print(\"• Statistical significance indicated by asterisks (* p<0.05, ** p<0.01, *** p<0.001)\")\n",
    "\n",
    "print(\"\\nFEATURE DESCRIPTIONS:\")\n",
    "for feature, desc in feature_descriptions.items():\n",
    "    print(f\"• {feature}: {desc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Data Preparation for Modeling\n",
    "\n",
    "### Purpose:\n",
    "- Prepare data for unsupervised models\n",
    "- Apply feature scaling\n",
    "- Create train/test split for evaluation\n",
    "- Save processed data for modeling phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA PREPARATION FOR UNSUPERVISED MODELING:\n",
      "============================================================\n",
      "Data prepared successfully!\n",
      "Original features: 140 time steps\n",
      "Engineered features: 12 statistical features\n",
      "Training samples: 1400\n",
      "Test samples: 600\n",
      "Training anomaly rate: 11.00%\n",
      "Test anomaly rate: 11.00%\n"
     ]
    }
   ],
   "source": [
    "# Prepare data for unsupervised modeling\n",
    "print(\"DATA PREPARATION FOR UNSUPERVISED MODELING:\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Prepare different feature sets\n",
    "X_original = X.copy()  # Original time series\n",
    "X_engineered = anomaly_features.drop('target', axis=1).values  # Engineered features\n",
    "\n",
    "# Scale features\n",
    "scaler_original = StandardScaler()\n",
    "scaler_engineered = StandardScaler()\n",
    "\n",
    "X_original_scaled = scaler_original.fit_transform(X_original)\n",
    "X_engineered_scaled = scaler_engineered.fit_transform(X_engineered)\n",
    "\n",
    "# Create train/test split for evaluation (labels only used for evaluation)\n",
    "X_train_orig, X_test_orig, y_train, y_test = train_test_split(\n",
    "    X_original_scaled, y, test_size=0.3, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "X_train_eng, X_test_eng, _, _ = train_test_split(\n",
    "    X_engineered_scaled, y, test_size=0.3, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "print(f\"Data prepared successfully!\")\n",
    "print(f\"Original features: {X_original.shape[1]} time steps\")\n",
    "print(f\"Engineered features: {X_engineered.shape[1]} statistical features\")\n",
    "print(f\"Training samples: {X_train_orig.shape[0]}\")\n",
    "print(f\"Test samples: {X_test_orig.shape[0]}\")\n",
    "print(f\"Training anomaly rate: {(y_train != 1).mean():.2%}\")\n",
    "print(f\"Test anomaly rate: {(y_test != 1).mean():.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data preparation and analysis complete!\n",
      "Ready for anomaly detection models.\n"
     ]
    }
   ],
   "source": [
    "# Data preparation complete\n",
    "print(\"Data preparation and analysis complete!\")\n",
    "print(\"Ready for anomaly detection models.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Summary and Key Findings\n",
    "\n",
    "### What we've accomplished:\n",
    "- Comprehensive data exploration\n",
    "- Feature engineering for anomaly detection\n",
    "- Data preparation for modeling\n",
    "- Insights for model development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EDA SUMMARY AND KEY FINDINGS:\n",
      "============================================================\n",
      "Dataset: 2000 samples, 140 time steps\n",
      "Anomaly rate: 11.00%\n",
      "Engineered features: 12\n",
      "Data quality: Excellent (no missing values, no duplicates)\n",
      "\n",
      "KEY INSIGHTS FOR ANOMALY DETECTION:\n",
      "============================================================\n",
      "1. Balanced dataset with 11% anomaly rate\n",
      "2. Clear visual differences between normal and anomaly patterns\n",
      "3. Statistical features show good separation potential\n",
      "4. MAD, peak count, and zero crossings are promising features\n",
      "5. Both original time series and engineered features available\n",
      "\n",
      "RECOMMENDATIONS FOR MODELING:\n",
      "============================================================\n",
      "1. Use MAD-based approach for rule-based model\n",
      "2. Try reconstruction-based methods for ML models\n",
      "3. Consider ensemble approaches combining multiple features\n",
      "4. Focus on interpretable models for real-world applications\n",
      "\n",
      "CORRELATION FINDINGS:\n",
      "============================================================\n",
      "• IQR and Range show very strong correlation (0.91)\n",
      "• Trend and MAD show moderate correlation (0.57)\n",
      "• Skewness and MAD show moderate correlation (0.47)\n",
      "• Kurtosis and Skewness show strong negative correlation (-0.75)\n",
      "• Consider feature selection to reduce redundancy\n",
      "\n",
      "EDA COMPLETE! Ready for modeling phase.\n",
      "Data saved in 'data/prepared_data.pkl' for next steps.\n"
     ]
    }
   ],
   "source": [
    "# Final summary\n",
    "print(\"EDA SUMMARY AND KEY FINDINGS:\")\n",
    "print(\"=\" * 60)\n",
    "print(f\"Dataset: {X.shape[0]} samples, {X.shape[1]} time steps\")\n",
    "print(f\"Anomaly rate: {anomaly_rate:.2%}\")\n",
    "print(f\"Engineered features: {len(anomaly_features.columns)-1}\")\n",
    "print(f\"Data quality: Excellent (no missing values, no duplicates)\")\n",
    "\n",
    "print(\"\\nKEY INSIGHTS FOR ANOMALY DETECTION:\")\n",
    "print(\"=\" * 60)\n",
    "print(\"1. Balanced dataset with 11% anomaly rate\")\n",
    "print(\"2. Clear visual differences between normal and anomaly patterns\")\n",
    "print(\"3. Statistical features show good separation potential\")\n",
    "print(\"4. MAD, peak count, and zero crossings are promising features\")\n",
    "print(\"5. Both original time series and engineered features available\")\n",
    "\n",
    "print(\"\\nRECOMMENDATIONS FOR MODELING:\")\n",
    "print(\"=\" * 60)\n",
    "print(\"1. Use MAD-based approach for rule-based model\")\n",
    "print(\"2. Try reconstruction-based methods for ML models\")\n",
    "print(\"3. Consider ensemble approaches combining multiple features\")\n",
    "print(\"4. Focus on interpretable models for real-world applications\")\n",
    "\n",
    "print(\"\\nCORRELATION FINDINGS:\")\n",
    "print(\"=\" * 60)\n",
    "print(\"• IQR and Range show very strong correlation (0.91)\")\n",
    "print(\"• Trend and MAD show moderate correlation (0.57)\")\n",
    "print(\"• Skewness and MAD show moderate correlation (0.47)\")\n",
    "print(\"• Kurtosis and Skewness show strong negative correlation (-0.75)\")\n",
    "print(\"• Consider feature selection to reduce redundancy\")\n",
    "\n",
    "print(\"\\nEDA COMPLETE! Ready for modeling phase.\")\n",
    "print(\"Data saved in 'data/prepared_data.pkl' for next steps.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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